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comapre two models

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Saeed Khosravi
2025-12-06 20:43:56 +01:00
parent fe6b805b31
commit 80ea363123
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280
LGBM_Tuning.py
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@@ -1,280 +0,0 @@
import itertools
import os
import random
import pandas as pd
import tqdm
from imblearn.over_sampling import KMeansSMOTE
from lightgbm import LGBMClassifier
from sklearn.metrics import (
accuracy_score,
f1_score,
fbeta_score,
precision_score,
recall_score,
)
from sklearn.model_selection import StratifiedKFold, train_test_split
from utils import scaling_handler
RESULTS_FILENAME = "results_lgbm_tuning.csv"
def get_metrics(y_true, y_pred, prefix=""):
metrics = {}
metrics[f"{prefix}accuracy"] = accuracy_score(y_true, y_pred)
metrics[f"{prefix}f1_macro"] = f1_score(y_true, y_pred, average="macro")
metrics[f"{prefix}f2_macro"] = fbeta_score(y_true, y_pred, beta=2, average="macro")
metrics[f"{prefix}recall_macro"] = recall_score(y_true, y_pred, average="macro")
metrics[f"{prefix}precision_macro"] = precision_score(
y_true, y_pred, average="macro"
)
f1_scores = f1_score(y_true, y_pred, average=None, zero_division=0)
f2_scores = fbeta_score(y_true, y_pred, beta=2, average=None, zero_division=0)
recall_scores = recall_score(y_true, y_pred, average=None, zero_division=0)
precision_scores = precision_score(y_true, y_pred, average=None, zero_division=0)
metrics[f"{prefix}f1_class0"] = f1_scores[0]
metrics[f"{prefix}f1_class1"] = f1_scores[1]
metrics[f"{prefix}f2_class0"] = f2_scores[0]
metrics[f"{prefix}f2_class1"] = f2_scores[1]
metrics[f"{prefix}recall_class0"] = recall_scores[0]
metrics[f"{prefix}recall_class1"] = recall_scores[1]
metrics[f"{prefix}precision_class0"] = precision_scores[0]
metrics[f"{prefix}precision_class1"] = precision_scores[1]
TP = sum((y_true == 1) & (y_pred == 1))
TN = sum((y_true == 0) & (y_pred == 0))
FP = sum((y_true == 0) & (y_pred == 1))
FN = sum((y_true == 1) & (y_pred == 0))
metrics[f"{prefix}TP"] = TP
metrics[f"{prefix}TN"] = TN
metrics[f"{prefix}FP"] = FP
metrics[f"{prefix}FN"] = FN
return metrics
try:
data_frame = pd.read_csv("./data/Ketamine_icp_no_missing.csv")
except FileNotFoundError:
print("Please ensure the data file exists at './data/Ketamine_icp_no_missing.csv'")
exit()
random_state = 42
n_split_kfold = 5
scaling_methods_list = [
"standard_scaling",
"robust_scaling",
"minmax_scaling",
"yeo_johnson",
]
boosting_type_list = ["gbdt", "dart"]
learning_rate_list = [0.03, 0.05, 0.1]
number_of_leaves_list = [100]
l2_regularization_lambda_list = [0.1, 0.5]
l1_regularization_alpha_list = [0.1, 0.5]
tree_subsample_tree_list = [0.8, 1.0]
subsample_list = [0.8, 1.0]
is_balanced_list = [True, False]
kmeans_smote_k_neighbors_list = [10]
kmeans_smote_n_clusters_list = [5]
param_combinations = list(
itertools.product(
scaling_methods_list,
boosting_type_list,
learning_rate_list,
number_of_leaves_list,
l2_regularization_lambda_list,
l1_regularization_alpha_list,
tree_subsample_tree_list,
subsample_list,
is_balanced_list,
kmeans_smote_k_neighbors_list,
kmeans_smote_n_clusters_list,
)
)
template_metrics = get_metrics(
pd.Series([0, 1, 0, 1]),
pd.Series([0, 1, 0, 1]),
)
template_cols = ["iteration", "model", "params"]
for k in template_metrics.keys():
template_cols.append(f"avg_val_{k}")
template_cols.append(f"test_{k}")
empty_df = pd.DataFrame(columns=template_cols)
if not os.path.exists(RESULTS_FILENAME):
empty_df.to_csv(RESULTS_FILENAME, index=False)
print(f"Initialized {RESULTS_FILENAME} with headers.")
else:
print(f"File {RESULTS_FILENAME} already exists. Appending to it.")
iteration = 0
for (
scaling_method,
boosting_type,
learning_rate,
num_leaves,
reg_lambda,
reg_alpha,
colsample_bytree,
subsample,
is_balanced,
k_neighbors,
kmeans_estimator,
) in tqdm.tqdm(param_combinations):
skf = StratifiedKFold(
n_splits=n_split_kfold, shuffle=True, random_state=random_state
)
data_frame_scaled = scaling_handler(data_frame, scaling_method)
y = data_frame_scaled["label"]
X = data_frame_scaled.drop(columns=["label"])
x_train_val, x_test, y_train_val, y_test = train_test_split(
X, y, test_size=0.15, stratify=y, random_state=random_state
)
fold_results = []
lgbm_classifier_params = None
sampling_method = "none"
for fold_idx, (train_index, val_index) in enumerate(
skf.split(x_train_val, y_train_val)
):
x_train_fold, x_val = x_train_val.iloc[train_index], x_train_val.iloc[val_index]
y_train_fold, y_val = y_train_val.iloc[train_index], y_train_val.iloc[val_index]
x_train = x_train_fold
y_train = y_train_fold
lgbm_classifier_params = None
lgbm_base_params = {
"boosting_type": boosting_type,
"objective": "binary",
"learning_rate": learning_rate,
"n_jobs": -1,
"num_leaves": num_leaves,
"reg_lambda": reg_lambda,
"reg_alpha": reg_alpha,
"colsample_bytree": colsample_bytree,
"subsample": subsample,
}
if is_balanced:
sampling_method = "KMeansSMOTE"
smote_params = {
"sampling_strategy": "minority",
"k_neighbors": k_neighbors,
"kmeans_estimator": kmeans_estimator,
"cluster_balance_threshold": 0.001,
"random_state": random_state,
"n_jobs": -1,
}
try:
smote = KMeansSMOTE(**smote_params)
x_train, y_train = smote.fit_resample(x_train_fold, y_train_fold)
lgbm_classifier_params = lgbm_base_params.copy()
except RuntimeError as e:
print(
f"KMeansSMOTE failed with RuntimeError in fold {fold_idx} of iteration {iteration}: {e}. Skipping fold."
)
continue
except ValueError as e:
print(
f"KMeansSMOTE failed with ValueError in fold {fold_idx} of iteration {iteration}: {e}. Skipping fold."
)
continue
else:
sampling_method = "class_weight"
class_1_weight = int(
(y_train_fold.shape[0] - y_train_fold.sum()) / y_train_fold.sum()
)
lgbm_classifier_params = lgbm_base_params.copy()
lgbm_classifier_params["class_weight"] = {0: 1, 1: class_1_weight}
if lgbm_classifier_params:
model = LGBMClassifier(
**lgbm_classifier_params, random_state=random_state, verbose=-1
)
model.fit(x_train, y_train)
y_pred_val = model.predict(x_val)
val_metrics = get_metrics(y_val, y_pred_val)
fold_results.append(val_metrics)
avg_val_metrics = {}
if fold_results:
val_df = pd.DataFrame(fold_results)
avg_val_metrics = val_df.mean().to_dict()
test_metrics = {}
if lgbm_classifier_params:
x_train_final = x_train_val
y_train_final = y_train_val
if is_balanced and sampling_method == "KMeansSMOTE":
try:
smote = KMeansSMOTE(**smote_params)
x_train_final, y_train_final = smote.fit_resample(
x_train_val, y_train_val
)
except (RuntimeError, ValueError) as e:
print(
f"Final KMeansSMOTE failed for iteration {iteration}: {e}. Skipping test evaluation."
)
lgbm_classifier_params = None
if lgbm_classifier_params:
final_lgbm_params = lgbm_base_params.copy()
test_model = LGBMClassifier(
**final_lgbm_params, random_state=random_state, verbose=-1
)
test_model.fit(x_train_final, y_train_final)
y_pred_test = test_model.predict(x_test)
test_metrics = get_metrics(y_test, y_pred_test, prefix="test_")
if lgbm_classifier_params:
params_str = str(lgbm_base_params).replace("}", "")
if is_balanced:
params_str += f", 'smote_k_neighbors': {k_neighbors}, 'smote_n_clusters': {kmeans_estimator}"
final_result_dict = {
"iteration": iteration,
"model": "LGBMClassifier",
"params": params_str
+ f", 'sampling_method': '{sampling_method}', 'scaling_method': '{scaling_method}'}}",
}
for k in template_metrics.keys():
final_result_dict[f"avg_val_{k}"] = avg_val_metrics.get(k, float("nan"))
final_result_dict[f"test_{k}"] = test_metrics.get(f"test_{k}", float("nan"))
result_row_df = pd.DataFrame([final_result_dict])
result_row_df = result_row_df.reindex(columns=template_cols, fill_value=None)
result_row_df.to_csv(RESULTS_FILENAME, mode="a", header=False, index=False)
iteration += 1
print(f"Finished: check {RESULTS_FILENAME}")

58
README.md
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@@ -101,11 +101,67 @@ Current results taken KMEANS_SMOTE:
| LGBM_KMEANS_SMOTE_knn10 | test | 0.9865689865689866 | 0.8543196878009516 | 0.8121616449258658 | 0.7895809912158687 | 0.9600745182511498 | 0.9931221342225928 | 0.7155172413793104 | 0.9964866786565728 | 0.6278366111951589 | 0.9987424020121568 | 0.5804195804195804 | 0.9875647668393782 | 0.9325842696629213 | 83 | 4765 | 6 | 60 |
## Tuning LightGBM and CatBoost
As it is written in `models/catboost_model.py` tune function for this model we used the following parameters:
```
scaling_methods = [
"standard_scaling",
"robust_scaling",
"minmax_scaling",
"yeo_johnson",
]
sampling_methods = [
"KMeansSMOTE",
"class_weight",
]
learning_rate_list = [0.03, 0.05, 0.1]
depth_list = [6, 8]
l2_leaf_reg_list = [1, 3]
subsample_list = [0.8, 1.0]
k_neighbors_list = [10]
kmeans_estimator_list = [5]
```
Also, for `models/lightgbm_model.py` tune function we used the folowing parameters:
```
scaling_methods = [
"standard_scaling",
"robust_scaling",
"minmax_scaling",
"yeo_johnson",
]
sampling_methods = [
"KMeansSMOTE",
"class_weight",
]
boosting_type_list = ["gbdt", "dart"]
learning_rate_list = [0.03, 0.05, 0.1]
number_of_leaves_list = [100]
l2_regularization_lambda_list = [0.1, 0.5]
l1_regularization_alpha_list = [0.1, 0.5]
tree_subsample_tree_list = [0.8, 1.0]
subsample_list = [0.8, 1.0]
kmeans_smote_k_neighbors_list = [10]
kmeans_smote_n_clusters_list = [5]
```
After tuning we train both models based on their best parameters and compare on an imbalanced test data.
here is the comparison results:
| model | accuracy | f1_macro | f2_macro | recall_macro | precision_macro | f1_class0 | f2_class0 | recall_class0 | precision_class0 | f1_class1 | f2_class1 | recall_class1 | precision_class1 | TP | TN | FP | FN |
|----------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|----|------|----|----|
| catboost | 0.9814814814814815 | 0.8195693865042805 | 0.8013174756506312 | 0.7903526990720451 | 0.8559205703525894 | 0.9904901243599122 | 0.9921698350221925 | 0.9932928107315029 | 0.9877032096706961 | 0.6486486486486487 | 0.6104651162790697 | 0.5874125874125874 | 0.7241379310344828 | 84 | 4739 | 32 | 59 |
| lightgbm | 0.9849409849409849 | 0.8469442386692707 | 0.8185917013944679 | 0.8023094072140393 | 0.9084632979829487 | 0.9922755741127348 | 0.9946427824048885 | 0.9962272060364703 | 0.9883551673944687 | 0.7016129032258065 | 0.6425406203840472 | 0.6083916083916084 | 0.8285714285714286 | 87 | 4753 | 18 | 56 |
## next steps:
```
✅ 1. Stratified K-fold only apply on train.
✅ 2. train LGBM model using KMEANS_SMOTE with knn k_neighbors=10 (fine-tune remained)
🗹 3. train Cat_boost using KMEANS_SMOTE with knn k_neighbors=10 (fine-tune remained)
✅ 3. train Cat_boost using KMEANS_SMOTE with knn k_neighbors=10 (fine-tune remained)
🗹 4. implement proposed methods of this article : https://1drv.ms/b/c/ab2a38fe5c318317/IQBEDsSFcYj6R6AMtOnh0X6DAZUlFqAYq19WT8nTeXomFwg
🗹 5. compare proposed model with SMOTE vs oversampling balancing method
```

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cat_boost_tuning_results.csv Normal file
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model,scaling_method,sampling_method,learning_rate,depth,l2_leaf_reg,subsample,k_neighbors,kmeans_estimator,accuracy,f1_macro,f2_macro,recall_macro,precision_macro,f1_class0,f2_class0,recall_class0,precision_class0,f1_class1,f2_class1,recall_class1,precision_class1,TP,TN,FP,FN
cat_boost,standard_scaling,KMeansSMOTE,0.03,6,1,0.8,10,5,0.986102098272271,0.8464211332649116,0.8023135099790324,0.7791138815061434,0.96203288693187,0.9928862049080953,0.9964579165143898,0.9988534818988754,0.9869903534333956,0.699956061621728,0.6081691034436749,0.5593742811134115,0.9370754204303445,90.4,5401.4,6.2,71.2
cat_boost,standard_scaling,KMeansSMOTE,0.03,6,1,1.0,10,5,0.9861020918246783,0.8469134138715712,0.803336559335327,0.7803076365169013,0.9596527280526976,0.9928857095450654,0.9964136002318558,0.9987795242415322,0.9870612190530006,0.7009411181980768,0.6102595184387981,0.5618357487922705,0.9322442370523942,90.8,5401.0,6.6,70.8
cat_boost,standard_scaling,KMeansSMOTE,0.03,6,3,0.8,10,5,0.9859943590013067,0.8455895471055779,0.8020371696150306,0.7790660796768216,0.9589476503888168,0.9928306747692883,0.9963693077576995,0.9987425419930116,0.9869888604171744,0.6983484194418677,0.6077050314723615,0.5593896173606318,0.9309064403604594,90.4,5400.8,6.8,71.2
cat_boost,standard_scaling,KMeansSMOTE,0.03,6,3,1.0,10,5,0.9861020982722708,0.8469259490456327,0.8033344354877547,0.7803038024550962,0.9598048908417075,0.9928856855497828,0.9964135841257609,0.998779524241532,0.9870612237987226,0.700966212541483,0.610255286849749,0.5618280806686603,0.9325485578846923,90.8,5401.0,6.6,70.8
cat_boost,standard_scaling,KMeansSMOTE,0.03,8,1,0.8,10,5,0.9863893900996572,0.8500036099146012,0.8060615973468563,0.7828660730636322,0.964231805061976,0.9930328042996537,0.9965611507017205,0.9989274532356148,0.9872078427726576,0.7069744155295486,0.6155620439919921,0.5668046928916495,0.9412557673512941,91.6,5401.8,5.8,70.0
cat_boost,standard_scaling,KMeansSMOTE,0.03,8,1,1.0,10,5,0.986245744185964,0.8476860128088646,0.8031711577107631,0.7797828081875858,0.9646225545136377,0.9929600993661,0.996553947866289,0.9989644354841356,0.9870279994042248,0.7024119262516294,0.6097883675552371,0.5606011808910359,0.9422171096230502,90.6,5402.0,5.6,71.0
cat_boost,standard_scaling,KMeansSMOTE,0.03,8,3,0.8,10,5,0.9863893900996572,0.8495220361155831,0.8050537352020507,0.7816569886453523,0.9659996628309043,0.9930333414905947,0.9966055373221451,0.9990014245723546,0.9871366794500623,0.7060107307405714,0.6135019330819563,0.5643125527183498,0.944862646211746,91.2,5402.2,5.4,70.4
cat_boost,standard_scaling,KMeansSMOTE,0.03,8,3,1.0,10,5,0.9863175639190143,0.8491915868997488,0.8053628818591857,0.7822264639269495,0.9631146491255429,0.9929960598572507,0.9965242639440666,0.9988904709870944,0.9871713330975938,0.7053871139422471,0.6142014997743052,0.5655624568668046,0.9390579651534919,91.4,5401.6,6.0,70.2
cat_boost,standard_scaling,KMeansSMOTE,0.05,6,1,0.8,10,5,0.9866407688367221,0.8556837421302268,0.8150894007705028,0.7931979762531072,0.9563290102657941,0.99315811122897,0.9963906992292945,0.9985575965519166,0.987817299311019,0.7182093730314834,0.633788102311711,0.5878383559542979,0.9248407212205692,95.0,5399.8,7.8,66.6
cat_boost,standard_scaling,KMeansSMOTE,0.05,6,1,1.0,10,5,0.9867484823173163,0.8559996996083085,0.8143727987698804,0.7920367073435457,0.960300275384561,0.9932142818892882,0.9965238018250784,0.9987425351533131,0.9877474074893051,0.7187851173273286,0.6322217957146827,0.585330879533778,0.9328531432798167,94.6,5400.8,6.8,67.0
cat_boost,standard_scaling,KMeansSMOTE,0.05,6,3,0.8,10,5,0.9864612227278926,0.8533450421041462,0.812224517306803,0.7901038917962598,0.9555993374147942,0.9930666965431355,0.9963539512475978,0.9985576033916148,0.9876360004981617,0.7136233876651569,0.6280950833660081,0.5816501802009049,0.9235626743314267,94.0,5399.8,7.8,67.6
cat_boost,standard_scaling,KMeansSMOTE,0.05,6,3,1.0,10,5,0.9867844083028228,0.8573006791580667,0.8164907232214175,0.7944656991807557,0.958193575687438,0.9932316312539872,0.9964645201920334,0.9986315747283545,0.9878901498488986,0.7213697270621464,0.6365169262508016,0.5902998236331569,0.9284970015259774,95.4,5400.2,7.4,66.2
cat_boost,standard_scaling,KMeansSMOTE,0.05,8,1,0.8,10,5,0.98696397375443,0.8588520652710141,0.8174395366148681,0.7951607816902195,0.9620975495653582,0.9933240141817763,0.9965900576766396,0.9987795037224373,0.9879284071915372,0.7243801163602518,0.6382890155530964,0.5915420596580018,0.9362666919391796,95.6,5401.0,6.6,66.0
cat_boost,standard_scaling,KMeansSMOTE,0.05,8,1,1.0,10,5,0.9869639802020224,0.8589256487158436,0.8174769428455269,0.7951646191718738,0.962012871399407,0.9933239605530348,0.996590061243829,0.9987795105621355,0.9879281234018163,0.7245273368786525,0.6383638244472246,0.5915497277816119,0.9360976193969981,95.6,5401.0,6.6,66.0
cat_boost,standard_scaling,KMeansSMOTE,0.05,8,3,0.8,10,5,0.9866407494939446,0.8547901540994214,0.8131230269576963,0.7907913130218117,0.9595074852665129,0.993159153169854,0.9964795006647693,0.9987055460650941,0.9876746003357422,0.7164211550289888,0.6297665532506237,0.5828770799785292,0.9313403701972834,94.2,5400.6,7.0,67.4
cat_boost,standard_scaling,KMeansSMOTE,0.05,8,3,1.0,10,5,0.9868562344834656,0.8578940042542003,0.816713699641437,0.7945065189109306,0.9597825930249886,0.9932686639522277,0.996523643388767,0.9987055460650941,0.9878908442470614,0.722519344556173,0.6369037558941069,0.5903074917567671,0.9316743418029161,95.4,5400.6,7.0,66.2
cat_boost,standard_scaling,KMeansSMOTE,0.1,6,1,0.8,10,5,0.9870357548019252,0.8623889925070234,0.8248272214187775,0.8041988095438739,0.9520721366121677,0.9933574241810342,0.9963161878839678,0.9982987002931768,0.9884657552032878,0.7314205608330123,0.6533382549535874,0.610098918794571,0.9156785180210474,98.6,5398.4,9.2,63.0
cat_boost,standard_scaling,KMeansSMOTE,0.1,6,1,1.0,10,5,0.9871435005204819,0.8625998575702166,0.8236953790986726,0.8024579181169808,0.956632062491588,0.9934137922982996,0.9964715073780287,0.9985206211430941,0.9883597026951225,0.7317859228421337,0.6509192508193165,0.6063952150908672,0.9249044222880534,98.0,5399.6,8.0,63.6
cat_boost,standard_scaling,KMeansSMOTE,0.1,6,3,0.8,10,5,0.9873948857051393,0.8667458053044029,0.829664336892599,0.8091970780277107,0.954337080418209,0.9935407771781357,0.9964341832568916,0.9983726853093131,0.988756309486377,0.7399508334306704,0.6628944905283063,0.6200214707461085,0.919917851350041,100.2,5398.8,8.8,61.4
cat_boost,standard_scaling,KMeansSMOTE,0.1,6,3,1.0,10,5,0.9872153395963098,0.8644394608691248,0.8267940039700823,0.8060953220274041,0.9540443270536809,0.9934492769479496,0.9963973749809257,0.9983726853093129,0.9885750243912155,0.7354296447903,0.6571906329592391,0.613817958745495,0.9195136297161464,99.2,5398.8,8.8,62.4
cat_boost,standard_scaling,KMeansSMOTE,0.1,8,1,0.8,10,5,0.9868203084979591,0.8599023912397564,0.8223149533336459,0.8016926983325823,0.9497489390690376,0.9932473069538978,0.9962276995711379,0.9982247494755321,0.9883200574357269,0.7265574755256153,0.6484022070961538,0.6051606471896328,0.9111778207023482,97.8,5398.0,9.6,63.8
cat_boost,standard_scaling,KMeansSMOTE,0.1,8,1,1.0,10,5,0.9870357999350727,0.8618063547824375,0.8234090356320497,0.8023986072625455,0.9541161878278945,0.9933581830944259,0.9963828328350818,0.9984096675578338,0.988358275605339,0.7302545264704492,0.6504352384290177,0.6063875469672572,0.91987410005045,98.0,5399.0,8.6,63.6
cat_boost,standard_scaling,KMeansSMOTE,0.1,8,3,0.8,10,5,0.9870716936826167,0.8625515302503806,0.8245004500539862,0.8036300100271925,0.953530205956364,0.9933761441894067,0.9963679530945301,0.9983726647902182,0.988430069482742,0.7317269163113542,0.6526329470134422,0.6088873552641669,0.9186303424299856,98.4,5398.8,8.8,63.2
cat_boost,standard_scaling,KMeansSMOTE,0.1,8,3,1.0,10,5,0.9869998868447514,0.8607931667292659,0.8214250075291298,0.7999887788946625,0.9563493266635918,0.9933405747125796,0.9964420595578707,0.9985206143033958,0.9882145666495015,0.7282457587459524,0.646407955500389,0.6014569434859289,0.9244840866776822,97.2,5399.6,8.0,64.4
cat_boost,standard_scaling,class_weight,0.03,6,1,0.8,10,5,0.986102098272271,0.8464211332649116,0.8023135099790324,0.7791138815061434,0.96203288693187,0.9928862049080953,0.9964579165143898,0.9988534818988754,0.9869903534333956,0.699956061621728,0.6081691034436749,0.5593742811134115,0.9370754204303445,90.4,5401.4,6.2,71.2
cat_boost,standard_scaling,class_weight,0.03,6,1,1.0,10,5,0.9861020918246783,0.8469134138715712,0.803336559335327,0.7803076365169013,0.9596527280526976,0.9928857095450654,0.9964136002318558,0.9987795242415322,0.9870612190530006,0.7009411181980768,0.6102595184387981,0.5618357487922705,0.9322442370523942,90.8,5401.0,6.6,70.8
cat_boost,standard_scaling,class_weight,0.03,6,3,0.8,10,5,0.9859943590013067,0.8455895471055779,0.8020371696150306,0.7790660796768216,0.9589476503888168,0.9928306747692883,0.9963693077576995,0.9987425419930116,0.9869888604171744,0.6983484194418677,0.6077050314723615,0.5593896173606318,0.9309064403604594,90.4,5400.8,6.8,71.2
cat_boost,standard_scaling,class_weight,0.03,6,3,1.0,10,5,0.9861020982722708,0.8469259490456327,0.8033344354877547,0.7803038024550962,0.9598048908417075,0.9928856855497828,0.9964135841257609,0.998779524241532,0.9870612237987226,0.700966212541483,0.610255286849749,0.5618280806686603,0.9325485578846923,90.8,5401.0,6.6,70.8
cat_boost,standard_scaling,class_weight,0.03,8,1,0.8,10,5,0.9863893900996572,0.8500036099146012,0.8060615973468563,0.7828660730636322,0.964231805061976,0.9930328042996537,0.9965611507017205,0.9989274532356148,0.9872078427726576,0.7069744155295486,0.6155620439919921,0.5668046928916495,0.9412557673512941,91.6,5401.8,5.8,70.0
cat_boost,standard_scaling,class_weight,0.03,8,1,1.0,10,5,0.986245744185964,0.8476860128088646,0.8031711577107631,0.7797828081875858,0.9646225545136377,0.9929600993661,0.996553947866289,0.9989644354841356,0.9870279994042248,0.7024119262516294,0.6097883675552371,0.5606011808910359,0.9422171096230502,90.6,5402.0,5.6,71.0
cat_boost,standard_scaling,class_weight,0.03,8,3,0.8,10,5,0.9863893900996572,0.8495220361155831,0.8050537352020507,0.7816569886453523,0.9659996628309043,0.9930333414905947,0.9966055373221451,0.9990014245723546,0.9871366794500623,0.7060107307405714,0.6135019330819563,0.5643125527183498,0.944862646211746,91.2,5402.2,5.4,70.4
cat_boost,standard_scaling,class_weight,0.03,8,3,1.0,10,5,0.9863175639190143,0.8491915868997488,0.8053628818591857,0.7822264639269495,0.9631146491255429,0.9929960598572507,0.9965242639440666,0.9988904709870944,0.9871713330975938,0.7053871139422471,0.6142014997743052,0.5655624568668046,0.9390579651534919,91.4,5401.6,6.0,70.2
cat_boost,standard_scaling,class_weight,0.05,6,1,0.8,10,5,0.9866407688367221,0.8556837421302268,0.8150894007705028,0.7931979762531072,0.9563290102657941,0.99315811122897,0.9963906992292945,0.9985575965519166,0.987817299311019,0.7182093730314834,0.633788102311711,0.5878383559542979,0.9248407212205692,95.0,5399.8,7.8,66.6
cat_boost,standard_scaling,class_weight,0.05,6,1,1.0,10,5,0.9867484823173163,0.8559996996083085,0.8143727987698804,0.7920367073435457,0.960300275384561,0.9932142818892882,0.9965238018250784,0.9987425351533131,0.9877474074893051,0.7187851173273286,0.6322217957146827,0.585330879533778,0.9328531432798167,94.6,5400.8,6.8,67.0
cat_boost,standard_scaling,class_weight,0.05,6,3,0.8,10,5,0.9864612227278926,0.8533450421041462,0.812224517306803,0.7901038917962598,0.9555993374147942,0.9930666965431355,0.9963539512475978,0.9985576033916148,0.9876360004981617,0.7136233876651569,0.6280950833660081,0.5816501802009049,0.9235626743314267,94.0,5399.8,7.8,67.6
cat_boost,standard_scaling,class_weight,0.05,6,3,1.0,10,5,0.9867844083028228,0.8573006791580667,0.8164907232214175,0.7944656991807557,0.958193575687438,0.9932316312539872,0.9964645201920334,0.9986315747283545,0.9878901498488986,0.7213697270621464,0.6365169262508016,0.5902998236331569,0.9284970015259774,95.4,5400.2,7.4,66.2
cat_boost,standard_scaling,class_weight,0.05,8,1,0.8,10,5,0.98696397375443,0.8588520652710141,0.8174395366148681,0.7951607816902195,0.9620975495653582,0.9933240141817763,0.9965900576766396,0.9987795037224373,0.9879284071915372,0.7243801163602518,0.6382890155530964,0.5915420596580018,0.9362666919391796,95.6,5401.0,6.6,66.0
cat_boost,standard_scaling,class_weight,0.05,8,1,1.0,10,5,0.9869639802020224,0.8589256487158436,0.8174769428455269,0.7951646191718738,0.962012871399407,0.9933239605530348,0.996590061243829,0.9987795105621355,0.9879281234018163,0.7245273368786525,0.6383638244472246,0.5915497277816119,0.9360976193969981,95.6,5401.0,6.6,66.0
cat_boost,standard_scaling,class_weight,0.05,8,3,0.8,10,5,0.9866407494939446,0.8547901540994214,0.8131230269576963,0.7907913130218117,0.9595074852665129,0.993159153169854,0.9964795006647693,0.9987055460650941,0.9876746003357422,0.7164211550289888,0.6297665532506237,0.5828770799785292,0.9313403701972834,94.2,5400.6,7.0,67.4
cat_boost,standard_scaling,class_weight,0.05,8,3,1.0,10,5,0.9868562344834656,0.8578940042542003,0.816713699641437,0.7945065189109306,0.9597825930249886,0.9932686639522277,0.996523643388767,0.9987055460650941,0.9878908442470614,0.722519344556173,0.6369037558941069,0.5903074917567671,0.9316743418029161,95.4,5400.6,7.0,66.2
cat_boost,standard_scaling,class_weight,0.1,6,1,0.8,10,5,0.9870357548019252,0.8623889925070234,0.8248272214187775,0.8041988095438739,0.9520721366121677,0.9933574241810342,0.9963161878839678,0.9982987002931768,0.9884657552032878,0.7314205608330123,0.6533382549535874,0.610098918794571,0.9156785180210474,98.6,5398.4,9.2,63.0
cat_boost,standard_scaling,class_weight,0.1,6,1,1.0,10,5,0.9871435005204819,0.8625998575702166,0.8236953790986726,0.8024579181169808,0.956632062491588,0.9934137922982996,0.9964715073780287,0.9985206211430941,0.9883597026951225,0.7317859228421337,0.6509192508193165,0.6063952150908672,0.9249044222880534,98.0,5399.6,8.0,63.6
cat_boost,standard_scaling,class_weight,0.1,6,3,0.8,10,5,0.9873948857051393,0.8667458053044029,0.829664336892599,0.8091970780277107,0.954337080418209,0.9935407771781357,0.9964341832568916,0.9983726853093131,0.988756309486377,0.7399508334306704,0.6628944905283063,0.6200214707461085,0.919917851350041,100.2,5398.8,8.8,61.4
cat_boost,standard_scaling,class_weight,0.1,6,3,1.0,10,5,0.9872153395963098,0.8644394608691248,0.8267940039700823,0.8060953220274041,0.9540443270536809,0.9934492769479496,0.9963973749809257,0.9983726853093129,0.9885750243912155,0.7354296447903,0.6571906329592391,0.613817958745495,0.9195136297161464,99.2,5398.8,8.8,62.4
cat_boost,standard_scaling,class_weight,0.1,8,1,0.8,10,5,0.9868203084979591,0.8599023912397564,0.8223149533336459,0.8016926983325823,0.9497489390690376,0.9932473069538978,0.9962276995711379,0.9982247494755321,0.9883200574357269,0.7265574755256153,0.6484022070961538,0.6051606471896328,0.9111778207023482,97.8,5398.0,9.6,63.8
cat_boost,standard_scaling,class_weight,0.1,8,1,1.0,10,5,0.9870357999350727,0.8618063547824375,0.8234090356320497,0.8023986072625455,0.9541161878278945,0.9933581830944259,0.9963828328350818,0.9984096675578338,0.988358275605339,0.7302545264704492,0.6504352384290177,0.6063875469672572,0.91987410005045,98.0,5399.0,8.6,63.6
cat_boost,standard_scaling,class_weight,0.1,8,3,0.8,10,5,0.9870716936826167,0.8625515302503806,0.8245004500539862,0.8036300100271925,0.953530205956364,0.9933761441894067,0.9963679530945301,0.9983726647902182,0.988430069482742,0.7317269163113542,0.6526329470134422,0.6088873552641669,0.9186303424299856,98.4,5398.8,8.8,63.2
cat_boost,standard_scaling,class_weight,0.1,8,3,1.0,10,5,0.9869998868447514,0.8607931667292659,0.8214250075291298,0.7999887788946625,0.9563493266635918,0.9933405747125796,0.9964420595578707,0.9985206143033958,0.9882145666495015,0.7282457587459524,0.646407955500389,0.6014569434859289,0.9244840866776822,97.2,5399.6,8.0,64.4
cat_boost,robust_scaling,KMeansSMOTE,0.03,6,1,0.8,10,5,0.986102098272271,0.8464211332649116,0.8023135099790324,0.7791138815061434,0.96203288693187,0.9928862049080953,0.9964579165143898,0.9988534818988754,0.9869903534333956,0.699956061621728,0.6081691034436749,0.5593742811134115,0.9370754204303445,90.4,5401.4,6.2,71.2
cat_boost,robust_scaling,KMeansSMOTE,0.03,6,1,1.0,10,5,0.9861020918246783,0.8469134138715712,0.803336559335327,0.7803076365169013,0.9596527280526976,0.9928857095450654,0.9964136002318558,0.9987795242415322,0.9870612190530006,0.7009411181980768,0.6102595184387981,0.5618357487922705,0.9322442370523942,90.8,5401.0,6.6,70.8
cat_boost,robust_scaling,KMeansSMOTE,0.03,6,3,0.8,10,5,0.9859943590013067,0.8455895471055779,0.8020371696150306,0.7790660796768216,0.9589476503888168,0.9928306747692883,0.9963693077576995,0.9987425419930116,0.9869888604171744,0.6983484194418677,0.6077050314723615,0.5593896173606318,0.9309064403604594,90.4,5400.8,6.8,71.2
cat_boost,robust_scaling,KMeansSMOTE,0.03,6,3,1.0,10,5,0.9861020982722708,0.8469259490456327,0.8033344354877547,0.7803038024550962,0.9598048908417075,0.9928856855497828,0.9964135841257609,0.998779524241532,0.9870612237987226,0.700966212541483,0.610255286849749,0.5618280806686603,0.9325485578846923,90.8,5401.0,6.6,70.8
cat_boost,robust_scaling,KMeansSMOTE,0.03,8,1,0.8,10,5,0.9863893900996572,0.8500036099146012,0.8060615973468563,0.7828660730636322,0.964231805061976,0.9930328042996537,0.9965611507017205,0.9989274532356148,0.9872078427726576,0.7069744155295486,0.6155620439919921,0.5668046928916495,0.9412557673512941,91.6,5401.8,5.8,70.0
cat_boost,robust_scaling,KMeansSMOTE,0.03,8,1,1.0,10,5,0.986245744185964,0.8476860128088646,0.8031711577107631,0.7797828081875858,0.9646225545136377,0.9929600993661,0.996553947866289,0.9989644354841356,0.9870279994042248,0.7024119262516294,0.6097883675552371,0.5606011808910359,0.9422171096230502,90.6,5402.0,5.6,71.0
cat_boost,robust_scaling,KMeansSMOTE,0.03,8,3,0.8,10,5,0.9863893900996572,0.8495220361155831,0.8050537352020507,0.7816569886453523,0.9659996628309043,0.9930333414905947,0.9966055373221451,0.9990014245723546,0.9871366794500623,0.7060107307405714,0.6135019330819563,0.5643125527183498,0.944862646211746,91.2,5402.2,5.4,70.4
cat_boost,robust_scaling,KMeansSMOTE,0.03,8,3,1.0,10,5,0.9863175639190143,0.8491915868997488,0.8053628818591857,0.7822264639269495,0.9631146491255429,0.9929960598572507,0.9965242639440666,0.9988904709870944,0.9871713330975938,0.7053871139422471,0.6142014997743052,0.5655624568668046,0.9390579651534919,91.4,5401.6,6.0,70.2
cat_boost,robust_scaling,KMeansSMOTE,0.05,6,1,0.8,10,5,0.9866407688367221,0.8556837421302268,0.8150894007705028,0.7931979762531072,0.9563290102657941,0.99315811122897,0.9963906992292945,0.9985575965519166,0.987817299311019,0.7182093730314834,0.633788102311711,0.5878383559542979,0.9248407212205692,95.0,5399.8,7.8,66.6
cat_boost,robust_scaling,KMeansSMOTE,0.05,6,1,1.0,10,5,0.9867484823173163,0.8559996996083085,0.8143727987698804,0.7920367073435457,0.960300275384561,0.9932142818892882,0.9965238018250784,0.9987425351533131,0.9877474074893051,0.7187851173273286,0.6322217957146827,0.585330879533778,0.9328531432798167,94.6,5400.8,6.8,67.0
cat_boost,robust_scaling,KMeansSMOTE,0.05,6,3,0.8,10,5,0.9864612227278926,0.8533450421041462,0.812224517306803,0.7901038917962598,0.9555993374147942,0.9930666965431355,0.9963539512475978,0.9985576033916148,0.9876360004981617,0.7136233876651569,0.6280950833660081,0.5816501802009049,0.9235626743314267,94.0,5399.8,7.8,67.6
cat_boost,robust_scaling,KMeansSMOTE,0.05,6,3,1.0,10,5,0.9867844083028228,0.8573006791580667,0.8164907232214175,0.7944656991807557,0.958193575687438,0.9932316312539872,0.9964645201920334,0.9986315747283545,0.9878901498488986,0.7213697270621464,0.6365169262508016,0.5902998236331569,0.9284970015259774,95.4,5400.2,7.4,66.2
cat_boost,robust_scaling,KMeansSMOTE,0.05,8,1,0.8,10,5,0.98696397375443,0.8588520652710141,0.8174395366148681,0.7951607816902195,0.9620975495653582,0.9933240141817763,0.9965900576766396,0.9987795037224373,0.9879284071915372,0.7243801163602518,0.6382890155530964,0.5915420596580018,0.9362666919391796,95.6,5401.0,6.6,66.0
cat_boost,robust_scaling,KMeansSMOTE,0.05,8,1,1.0,10,5,0.9869639802020224,0.8589256487158436,0.8174769428455269,0.7951646191718738,0.962012871399407,0.9933239605530348,0.996590061243829,0.9987795105621355,0.9879281234018163,0.7245273368786525,0.6383638244472246,0.5915497277816119,0.9360976193969981,95.6,5401.0,6.6,66.0
cat_boost,robust_scaling,KMeansSMOTE,0.05,8,3,0.8,10,5,0.9866407494939446,0.8547901540994214,0.8131230269576963,0.7907913130218117,0.9595074852665129,0.993159153169854,0.9964795006647693,0.9987055460650941,0.9876746003357422,0.7164211550289888,0.6297665532506237,0.5828770799785292,0.9313403701972834,94.2,5400.6,7.0,67.4
cat_boost,robust_scaling,KMeansSMOTE,0.05,8,3,1.0,10,5,0.9868562344834656,0.8578940042542003,0.816713699641437,0.7945065189109306,0.9597825930249886,0.9932686639522277,0.996523643388767,0.9987055460650941,0.9878908442470614,0.722519344556173,0.6369037558941069,0.5903074917567671,0.9316743418029161,95.4,5400.6,7.0,66.2
cat_boost,robust_scaling,KMeansSMOTE,0.1,6,1,0.8,10,5,0.9870357548019252,0.8623889925070234,0.8248272214187775,0.8041988095438739,0.9520721366121677,0.9933574241810342,0.9963161878839678,0.9982987002931768,0.9884657552032878,0.7314205608330123,0.6533382549535874,0.610098918794571,0.9156785180210474,98.6,5398.4,9.2,63.0
cat_boost,robust_scaling,KMeansSMOTE,0.1,6,1,1.0,10,5,0.9871435005204819,0.8625998575702166,0.8236953790986726,0.8024579181169808,0.956632062491588,0.9934137922982996,0.9964715073780287,0.9985206211430941,0.9883597026951225,0.7317859228421337,0.6509192508193165,0.6063952150908672,0.9249044222880534,98.0,5399.6,8.0,63.6
cat_boost,robust_scaling,KMeansSMOTE,0.1,6,3,0.8,10,5,0.9873948857051393,0.8667458053044029,0.829664336892599,0.8091970780277107,0.954337080418209,0.9935407771781357,0.9964341832568916,0.9983726853093131,0.988756309486377,0.7399508334306704,0.6628944905283063,0.6200214707461085,0.919917851350041,100.2,5398.8,8.8,61.4
cat_boost,robust_scaling,KMeansSMOTE,0.1,6,3,1.0,10,5,0.9872153395963098,0.8644394608691248,0.8267940039700823,0.8060953220274041,0.9540443270536809,0.9934492769479496,0.9963973749809257,0.9983726853093129,0.9885750243912155,0.7354296447903,0.6571906329592391,0.613817958745495,0.9195136297161464,99.2,5398.8,8.8,62.4
cat_boost,robust_scaling,KMeansSMOTE,0.1,8,1,0.8,10,5,0.9868203084979591,0.8599023912397564,0.8223149533336459,0.8016926983325823,0.9497489390690376,0.9932473069538978,0.9962276995711379,0.9982247494755321,0.9883200574357269,0.7265574755256153,0.6484022070961538,0.6051606471896328,0.9111778207023482,97.8,5398.0,9.6,63.8
cat_boost,robust_scaling,KMeansSMOTE,0.1,8,1,1.0,10,5,0.9870357999350727,0.8618063547824375,0.8234090356320497,0.8023986072625455,0.9541161878278945,0.9933581830944259,0.9963828328350818,0.9984096675578338,0.988358275605339,0.7302545264704492,0.6504352384290177,0.6063875469672572,0.91987410005045,98.0,5399.0,8.6,63.6
cat_boost,robust_scaling,KMeansSMOTE,0.1,8,3,0.8,10,5,0.9870716936826167,0.8625515302503806,0.8245004500539862,0.8036300100271925,0.953530205956364,0.9933761441894067,0.9963679530945301,0.9983726647902182,0.988430069482742,0.7317269163113542,0.6526329470134422,0.6088873552641669,0.9186303424299856,98.4,5398.8,8.8,63.2
cat_boost,robust_scaling,KMeansSMOTE,0.1,8,3,1.0,10,5,0.9869998868447514,0.8607931667292659,0.8214250075291298,0.7999887788946625,0.9563493266635918,0.9933405747125796,0.9964420595578707,0.9985206143033958,0.9882145666495015,0.7282457587459524,0.646407955500389,0.6014569434859289,0.9244840866776822,97.2,5399.6,8.0,64.4
cat_boost,robust_scaling,class_weight,0.03,6,1,0.8,10,5,0.986102098272271,0.8464211332649116,0.8023135099790324,0.7791138815061434,0.96203288693187,0.9928862049080953,0.9964579165143898,0.9988534818988754,0.9869903534333956,0.699956061621728,0.6081691034436749,0.5593742811134115,0.9370754204303445,90.4,5401.4,6.2,71.2
cat_boost,robust_scaling,class_weight,0.03,6,1,1.0,10,5,0.9861020918246783,0.8469134138715712,0.803336559335327,0.7803076365169013,0.9596527280526976,0.9928857095450654,0.9964136002318558,0.9987795242415322,0.9870612190530006,0.7009411181980768,0.6102595184387981,0.5618357487922705,0.9322442370523942,90.8,5401.0,6.6,70.8
cat_boost,robust_scaling,class_weight,0.03,6,3,0.8,10,5,0.9859943590013067,0.8455895471055779,0.8020371696150306,0.7790660796768216,0.9589476503888168,0.9928306747692883,0.9963693077576995,0.9987425419930116,0.9869888604171744,0.6983484194418677,0.6077050314723615,0.5593896173606318,0.9309064403604594,90.4,5400.8,6.8,71.2
cat_boost,robust_scaling,class_weight,0.03,6,3,1.0,10,5,0.9861020982722708,0.8469259490456327,0.8033344354877547,0.7803038024550962,0.9598048908417075,0.9928856855497828,0.9964135841257609,0.998779524241532,0.9870612237987226,0.700966212541483,0.610255286849749,0.5618280806686603,0.9325485578846923,90.8,5401.0,6.6,70.8
cat_boost,robust_scaling,class_weight,0.03,8,1,0.8,10,5,0.9863893900996572,0.8500036099146012,0.8060615973468563,0.7828660730636322,0.964231805061976,0.9930328042996537,0.9965611507017205,0.9989274532356148,0.9872078427726576,0.7069744155295486,0.6155620439919921,0.5668046928916495,0.9412557673512941,91.6,5401.8,5.8,70.0
cat_boost,robust_scaling,class_weight,0.03,8,1,1.0,10,5,0.986245744185964,0.8476860128088646,0.8031711577107631,0.7797828081875858,0.9646225545136377,0.9929600993661,0.996553947866289,0.9989644354841356,0.9870279994042248,0.7024119262516294,0.6097883675552371,0.5606011808910359,0.9422171096230502,90.6,5402.0,5.6,71.0
cat_boost,robust_scaling,class_weight,0.03,8,3,0.8,10,5,0.9863893900996572,0.8495220361155831,0.8050537352020507,0.7816569886453523,0.9659996628309043,0.9930333414905947,0.9966055373221451,0.9990014245723546,0.9871366794500623,0.7060107307405714,0.6135019330819563,0.5643125527183498,0.944862646211746,91.2,5402.2,5.4,70.4
cat_boost,robust_scaling,class_weight,0.03,8,3,1.0,10,5,0.9863175639190143,0.8491915868997488,0.8053628818591857,0.7822264639269495,0.9631146491255429,0.9929960598572507,0.9965242639440666,0.9988904709870944,0.9871713330975938,0.7053871139422471,0.6142014997743052,0.5655624568668046,0.9390579651534919,91.4,5401.6,6.0,70.2
cat_boost,robust_scaling,class_weight,0.05,6,1,0.8,10,5,0.9866407688367221,0.8556837421302268,0.8150894007705028,0.7931979762531072,0.9563290102657941,0.99315811122897,0.9963906992292945,0.9985575965519166,0.987817299311019,0.7182093730314834,0.633788102311711,0.5878383559542979,0.9248407212205692,95.0,5399.8,7.8,66.6
cat_boost,robust_scaling,class_weight,0.05,6,1,1.0,10,5,0.9867484823173163,0.8559996996083085,0.8143727987698804,0.7920367073435457,0.960300275384561,0.9932142818892882,0.9965238018250784,0.9987425351533131,0.9877474074893051,0.7187851173273286,0.6322217957146827,0.585330879533778,0.9328531432798167,94.6,5400.8,6.8,67.0
cat_boost,robust_scaling,class_weight,0.05,6,3,0.8,10,5,0.9864612227278926,0.8533450421041462,0.812224517306803,0.7901038917962598,0.9555993374147942,0.9930666965431355,0.9963539512475978,0.9985576033916148,0.9876360004981617,0.7136233876651569,0.6280950833660081,0.5816501802009049,0.9235626743314267,94.0,5399.8,7.8,67.6
cat_boost,robust_scaling,class_weight,0.05,6,3,1.0,10,5,0.9867844083028228,0.8573006791580667,0.8164907232214175,0.7944656991807557,0.958193575687438,0.9932316312539872,0.9964645201920334,0.9986315747283545,0.9878901498488986,0.7213697270621464,0.6365169262508016,0.5902998236331569,0.9284970015259774,95.4,5400.2,7.4,66.2
cat_boost,robust_scaling,class_weight,0.05,8,1,0.8,10,5,0.98696397375443,0.8588520652710141,0.8174395366148681,0.7951607816902195,0.9620975495653582,0.9933240141817763,0.9965900576766396,0.9987795037224373,0.9879284071915372,0.7243801163602518,0.6382890155530964,0.5915420596580018,0.9362666919391796,95.6,5401.0,6.6,66.0
cat_boost,robust_scaling,class_weight,0.05,8,1,1.0,10,5,0.9869639802020224,0.8589256487158436,0.8174769428455269,0.7951646191718738,0.962012871399407,0.9933239605530348,0.996590061243829,0.9987795105621355,0.9879281234018163,0.7245273368786525,0.6383638244472246,0.5915497277816119,0.9360976193969981,95.6,5401.0,6.6,66.0
cat_boost,robust_scaling,class_weight,0.05,8,3,0.8,10,5,0.9866407494939446,0.8547901540994214,0.8131230269576963,0.7907913130218117,0.9595074852665129,0.993159153169854,0.9964795006647693,0.9987055460650941,0.9876746003357422,0.7164211550289888,0.6297665532506237,0.5828770799785292,0.9313403701972834,94.2,5400.6,7.0,67.4
cat_boost,robust_scaling,class_weight,0.05,8,3,1.0,10,5,0.9868562344834656,0.8578940042542003,0.816713699641437,0.7945065189109306,0.9597825930249886,0.9932686639522277,0.996523643388767,0.9987055460650941,0.9878908442470614,0.722519344556173,0.6369037558941069,0.5903074917567671,0.9316743418029161,95.4,5400.6,7.0,66.2
cat_boost,robust_scaling,class_weight,0.1,6,1,0.8,10,5,0.9870357548019252,0.8623889925070234,0.8248272214187775,0.8041988095438739,0.9520721366121677,0.9933574241810342,0.9963161878839678,0.9982987002931768,0.9884657552032878,0.7314205608330123,0.6533382549535874,0.610098918794571,0.9156785180210474,98.6,5398.4,9.2,63.0
cat_boost,robust_scaling,class_weight,0.1,6,1,1.0,10,5,0.9871435005204819,0.8625998575702166,0.8236953790986726,0.8024579181169808,0.956632062491588,0.9934137922982996,0.9964715073780287,0.9985206211430941,0.9883597026951225,0.7317859228421337,0.6509192508193165,0.6063952150908672,0.9249044222880534,98.0,5399.6,8.0,63.6
cat_boost,robust_scaling,class_weight,0.1,6,3,0.8,10,5,0.9873948857051393,0.8667458053044029,0.829664336892599,0.8091970780277107,0.954337080418209,0.9935407771781357,0.9964341832568916,0.9983726853093131,0.988756309486377,0.7399508334306704,0.6628944905283063,0.6200214707461085,0.919917851350041,100.2,5398.8,8.8,61.4
cat_boost,robust_scaling,class_weight,0.1,6,3,1.0,10,5,0.9872153395963098,0.8644394608691248,0.8267940039700823,0.8060953220274041,0.9540443270536809,0.9934492769479496,0.9963973749809257,0.9983726853093129,0.9885750243912155,0.7354296447903,0.6571906329592391,0.613817958745495,0.9195136297161464,99.2,5398.8,8.8,62.4
cat_boost,robust_scaling,class_weight,0.1,8,1,0.8,10,5,0.9868203084979591,0.8599023912397564,0.8223149533336459,0.8016926983325823,0.9497489390690376,0.9932473069538978,0.9962276995711379,0.9982247494755321,0.9883200574357269,0.7265574755256153,0.6484022070961538,0.6051606471896328,0.9111778207023482,97.8,5398.0,9.6,63.8
cat_boost,robust_scaling,class_weight,0.1,8,1,1.0,10,5,0.9870357999350727,0.8618063547824375,0.8234090356320497,0.8023986072625455,0.9541161878278945,0.9933581830944259,0.9963828328350818,0.9984096675578338,0.988358275605339,0.7302545264704492,0.6504352384290177,0.6063875469672572,0.91987410005045,98.0,5399.0,8.6,63.6
cat_boost,robust_scaling,class_weight,0.1,8,3,0.8,10,5,0.9870716936826167,0.8625515302503806,0.8245004500539862,0.8036300100271925,0.953530205956364,0.9933761441894067,0.9963679530945301,0.9983726647902182,0.988430069482742,0.7317269163113542,0.6526329470134422,0.6088873552641669,0.9186303424299856,98.4,5398.8,8.8,63.2
cat_boost,robust_scaling,class_weight,0.1,8,3,1.0,10,5,0.9869998868447514,0.8607931667292659,0.8214250075291298,0.7999887788946625,0.9563493266635918,0.9933405747125796,0.9964420595578707,0.9985206143033958,0.9882145666495015,0.7282457587459524,0.646407955500389,0.6014569434859289,0.9244840866776822,97.2,5399.6,8.0,64.4
cat_boost,minmax_scaling,KMeansSMOTE,0.03,6,1,0.8,10,5,0.986102098272271,0.8464211332649116,0.8023135099790324,0.7791138815061434,0.96203288693187,0.9928862049080953,0.9964579165143898,0.9988534818988754,0.9869903534333956,0.699956061621728,0.6081691034436749,0.5593742811134115,0.9370754204303445,90.4,5401.4,6.2,71.2
cat_boost,minmax_scaling,KMeansSMOTE,0.03,6,1,1.0,10,5,0.9861020918246783,0.8469134138715712,0.803336559335327,0.7803076365169013,0.9596527280526976,0.9928857095450654,0.9964136002318558,0.9987795242415322,0.9870612190530006,0.7009411181980768,0.6102595184387981,0.5618357487922705,0.9322442370523942,90.8,5401.0,6.6,70.8
cat_boost,minmax_scaling,KMeansSMOTE,0.03,6,3,0.8,10,5,0.9859943590013067,0.8455895471055779,0.8020371696150306,0.7790660796768216,0.9589476503888168,0.9928306747692883,0.9963693077576995,0.9987425419930116,0.9869888604171744,0.6983484194418677,0.6077050314723615,0.5593896173606318,0.9309064403604594,90.4,5400.8,6.8,71.2
cat_boost,minmax_scaling,KMeansSMOTE,0.03,6,3,1.0,10,5,0.9861020982722708,0.8469259490456327,0.8033344354877547,0.7803038024550962,0.9598048908417075,0.9928856855497828,0.9964135841257609,0.998779524241532,0.9870612237987226,0.700966212541483,0.610255286849749,0.5618280806686603,0.9325485578846923,90.8,5401.0,6.6,70.8
cat_boost,minmax_scaling,KMeansSMOTE,0.03,8,1,0.8,10,5,0.9863893900996572,0.8500036099146012,0.8060615973468563,0.7828660730636322,0.964231805061976,0.9930328042996537,0.9965611507017205,0.9989274532356148,0.9872078427726576,0.7069744155295486,0.6155620439919921,0.5668046928916495,0.9412557673512941,91.6,5401.8,5.8,70.0
cat_boost,minmax_scaling,KMeansSMOTE,0.03,8,1,1.0,10,5,0.986245744185964,0.8476860128088646,0.8031711577107631,0.7797828081875858,0.9646225545136377,0.9929600993661,0.996553947866289,0.9989644354841356,0.9870279994042248,0.7024119262516294,0.6097883675552371,0.5606011808910359,0.9422171096230502,90.6,5402.0,5.6,71.0
cat_boost,minmax_scaling,KMeansSMOTE,0.03,8,3,0.8,10,5,0.9863893900996572,0.8495220361155831,0.8050537352020507,0.7816569886453523,0.9659996628309043,0.9930333414905947,0.9966055373221451,0.9990014245723546,0.9871366794500623,0.7060107307405714,0.6135019330819563,0.5643125527183498,0.944862646211746,91.2,5402.2,5.4,70.4
cat_boost,minmax_scaling,KMeansSMOTE,0.03,8,3,1.0,10,5,0.9863175639190143,0.8491915868997488,0.8053628818591857,0.7822264639269495,0.9631146491255429,0.9929960598572507,0.9965242639440666,0.9988904709870944,0.9871713330975938,0.7053871139422471,0.6142014997743052,0.5655624568668046,0.9390579651534919,91.4,5401.6,6.0,70.2
cat_boost,minmax_scaling,KMeansSMOTE,0.05,6,1,0.8,10,5,0.9866407688367221,0.8556837421302268,0.8150894007705028,0.7931979762531072,0.9563290102657941,0.99315811122897,0.9963906992292945,0.9985575965519166,0.987817299311019,0.7182093730314834,0.633788102311711,0.5878383559542979,0.9248407212205692,95.0,5399.8,7.8,66.6
cat_boost,minmax_scaling,KMeansSMOTE,0.05,6,1,1.0,10,5,0.9867484823173163,0.8559996996083085,0.8143727987698804,0.7920367073435457,0.960300275384561,0.9932142818892882,0.9965238018250784,0.9987425351533131,0.9877474074893051,0.7187851173273286,0.6322217957146827,0.585330879533778,0.9328531432798167,94.6,5400.8,6.8,67.0
cat_boost,minmax_scaling,KMeansSMOTE,0.05,6,3,0.8,10,5,0.9864612227278926,0.8533450421041462,0.812224517306803,0.7901038917962598,0.9555993374147942,0.9930666965431355,0.9963539512475978,0.9985576033916148,0.9876360004981617,0.7136233876651569,0.6280950833660081,0.5816501802009049,0.9235626743314267,94.0,5399.8,7.8,67.6
cat_boost,minmax_scaling,KMeansSMOTE,0.05,6,3,1.0,10,5,0.9867844083028228,0.8573006791580667,0.8164907232214175,0.7944656991807557,0.958193575687438,0.9932316312539872,0.9964645201920334,0.9986315747283545,0.9878901498488986,0.7213697270621464,0.6365169262508016,0.5902998236331569,0.9284970015259774,95.4,5400.2,7.4,66.2
cat_boost,minmax_scaling,KMeansSMOTE,0.05,8,1,0.8,10,5,0.98696397375443,0.8588520652710141,0.8174395366148681,0.7951607816902195,0.9620975495653582,0.9933240141817763,0.9965900576766396,0.9987795037224373,0.9879284071915372,0.7243801163602518,0.6382890155530964,0.5915420596580018,0.9362666919391796,95.6,5401.0,6.6,66.0
cat_boost,minmax_scaling,KMeansSMOTE,0.05,8,1,1.0,10,5,0.9869639802020224,0.8589256487158436,0.8174769428455269,0.7951646191718738,0.962012871399407,0.9933239605530348,0.996590061243829,0.9987795105621355,0.9879281234018163,0.7245273368786525,0.6383638244472246,0.5915497277816119,0.9360976193969981,95.6,5401.0,6.6,66.0
cat_boost,minmax_scaling,KMeansSMOTE,0.05,8,3,0.8,10,5,0.9866407494939446,0.8547901540994214,0.8131230269576963,0.7907913130218117,0.9595074852665129,0.993159153169854,0.9964795006647693,0.9987055460650941,0.9876746003357422,0.7164211550289888,0.6297665532506237,0.5828770799785292,0.9313403701972834,94.2,5400.6,7.0,67.4
cat_boost,minmax_scaling,KMeansSMOTE,0.05,8,3,1.0,10,5,0.9868562344834656,0.8578940042542003,0.816713699641437,0.7945065189109306,0.9597825930249886,0.9932686639522277,0.996523643388767,0.9987055460650941,0.9878908442470614,0.722519344556173,0.6369037558941069,0.5903074917567671,0.9316743418029161,95.4,5400.6,7.0,66.2
cat_boost,minmax_scaling,KMeansSMOTE,0.1,6,1,0.8,10,5,0.9870357548019252,0.8623889925070234,0.8248272214187775,0.8041988095438739,0.9520721366121677,0.9933574241810342,0.9963161878839678,0.9982987002931768,0.9884657552032878,0.7314205608330123,0.6533382549535874,0.610098918794571,0.9156785180210474,98.6,5398.4,9.2,63.0
cat_boost,minmax_scaling,KMeansSMOTE,0.1,6,1,1.0,10,5,0.9871435005204819,0.8625998575702166,0.8236953790986726,0.8024579181169808,0.956632062491588,0.9934137922982996,0.9964715073780287,0.9985206211430941,0.9883597026951225,0.7317859228421337,0.6509192508193165,0.6063952150908672,0.9249044222880534,98.0,5399.6,8.0,63.6
cat_boost,minmax_scaling,KMeansSMOTE,0.1,6,3,0.8,10,5,0.9873948857051393,0.8667458053044029,0.829664336892599,0.8091970780277107,0.954337080418209,0.9935407771781357,0.9964341832568916,0.9983726853093131,0.988756309486377,0.7399508334306704,0.6628944905283063,0.6200214707461085,0.919917851350041,100.2,5398.8,8.8,61.4
cat_boost,minmax_scaling,KMeansSMOTE,0.1,6,3,1.0,10,5,0.9872153395963098,0.8644394608691248,0.8267940039700823,0.8060953220274041,0.9540443270536809,0.9934492769479496,0.9963973749809257,0.9983726853093129,0.9885750243912155,0.7354296447903,0.6571906329592391,0.613817958745495,0.9195136297161464,99.2,5398.8,8.8,62.4
cat_boost,minmax_scaling,KMeansSMOTE,0.1,8,1,0.8,10,5,0.9868203084979591,0.8599023912397564,0.8223149533336459,0.8016926983325823,0.9497489390690376,0.9932473069538978,0.9962276995711379,0.9982247494755321,0.9883200574357269,0.7265574755256153,0.6484022070961538,0.6051606471896328,0.9111778207023482,97.8,5398.0,9.6,63.8
cat_boost,minmax_scaling,KMeansSMOTE,0.1,8,1,1.0,10,5,0.9870357999350727,0.8618063547824375,0.8234090356320497,0.8023986072625455,0.9541161878278945,0.9933581830944259,0.9963828328350818,0.9984096675578338,0.988358275605339,0.7302545264704492,0.6504352384290177,0.6063875469672572,0.91987410005045,98.0,5399.0,8.6,63.6
cat_boost,minmax_scaling,KMeansSMOTE,0.1,8,3,0.8,10,5,0.9870716936826167,0.8625515302503806,0.8245004500539862,0.8036300100271925,0.953530205956364,0.9933761441894067,0.9963679530945301,0.9983726647902182,0.988430069482742,0.7317269163113542,0.6526329470134422,0.6088873552641669,0.9186303424299856,98.4,5398.8,8.8,63.2
cat_boost,minmax_scaling,KMeansSMOTE,0.1,8,3,1.0,10,5,0.9869998868447514,0.8607931667292659,0.8214250075291298,0.7999887788946625,0.9563493266635918,0.9933405747125796,0.9964420595578707,0.9985206143033958,0.9882145666495015,0.7282457587459524,0.646407955500389,0.6014569434859289,0.9244840866776822,97.2,5399.6,8.0,64.4
cat_boost,minmax_scaling,class_weight,0.03,6,1,0.8,10,5,0.986102098272271,0.8464211332649116,0.8023135099790324,0.7791138815061434,0.96203288693187,0.9928862049080953,0.9964579165143898,0.9988534818988754,0.9869903534333956,0.699956061621728,0.6081691034436749,0.5593742811134115,0.9370754204303445,90.4,5401.4,6.2,71.2
cat_boost,minmax_scaling,class_weight,0.03,6,1,1.0,10,5,0.9861020918246783,0.8469134138715712,0.803336559335327,0.7803076365169013,0.9596527280526976,0.9928857095450654,0.9964136002318558,0.9987795242415322,0.9870612190530006,0.7009411181980768,0.6102595184387981,0.5618357487922705,0.9322442370523942,90.8,5401.0,6.6,70.8
cat_boost,minmax_scaling,class_weight,0.03,6,3,0.8,10,5,0.9859943590013067,0.8455895471055779,0.8020371696150306,0.7790660796768216,0.9589476503888168,0.9928306747692883,0.9963693077576995,0.9987425419930116,0.9869888604171744,0.6983484194418677,0.6077050314723615,0.5593896173606318,0.9309064403604594,90.4,5400.8,6.8,71.2
cat_boost,minmax_scaling,class_weight,0.03,6,3,1.0,10,5,0.9861020982722708,0.8469259490456327,0.8033344354877547,0.7803038024550962,0.9598048908417075,0.9928856855497828,0.9964135841257609,0.998779524241532,0.9870612237987226,0.700966212541483,0.610255286849749,0.5618280806686603,0.9325485578846923,90.8,5401.0,6.6,70.8
cat_boost,minmax_scaling,class_weight,0.03,8,1,0.8,10,5,0.9863893900996572,0.8500036099146012,0.8060615973468563,0.7828660730636322,0.964231805061976,0.9930328042996537,0.9965611507017205,0.9989274532356148,0.9872078427726576,0.7069744155295486,0.6155620439919921,0.5668046928916495,0.9412557673512941,91.6,5401.8,5.8,70.0
cat_boost,minmax_scaling,class_weight,0.03,8,1,1.0,10,5,0.986245744185964,0.8476860128088646,0.8031711577107631,0.7797828081875858,0.9646225545136377,0.9929600993661,0.996553947866289,0.9989644354841356,0.9870279994042248,0.7024119262516294,0.6097883675552371,0.5606011808910359,0.9422171096230502,90.6,5402.0,5.6,71.0
cat_boost,minmax_scaling,class_weight,0.03,8,3,0.8,10,5,0.9863893900996572,0.8495220361155831,0.8050537352020507,0.7816569886453523,0.9659996628309043,0.9930333414905947,0.9966055373221451,0.9990014245723546,0.9871366794500623,0.7060107307405714,0.6135019330819563,0.5643125527183498,0.944862646211746,91.2,5402.2,5.4,70.4
cat_boost,minmax_scaling,class_weight,0.03,8,3,1.0,10,5,0.9863175639190143,0.8491915868997488,0.8053628818591857,0.7822264639269495,0.9631146491255429,0.9929960598572507,0.9965242639440666,0.9988904709870944,0.9871713330975938,0.7053871139422471,0.6142014997743052,0.5655624568668046,0.9390579651534919,91.4,5401.6,6.0,70.2
cat_boost,minmax_scaling,class_weight,0.05,6,1,0.8,10,5,0.9866407688367221,0.8556837421302268,0.8150894007705028,0.7931979762531072,0.9563290102657941,0.99315811122897,0.9963906992292945,0.9985575965519166,0.987817299311019,0.7182093730314834,0.633788102311711,0.5878383559542979,0.9248407212205692,95.0,5399.8,7.8,66.6
cat_boost,minmax_scaling,class_weight,0.05,6,1,1.0,10,5,0.9867484823173163,0.8559996996083085,0.8143727987698804,0.7920367073435457,0.960300275384561,0.9932142818892882,0.9965238018250784,0.9987425351533131,0.9877474074893051,0.7187851173273286,0.6322217957146827,0.585330879533778,0.9328531432798167,94.6,5400.8,6.8,67.0
cat_boost,minmax_scaling,class_weight,0.05,6,3,0.8,10,5,0.9864612227278926,0.8533450421041462,0.812224517306803,0.7901038917962598,0.9555993374147942,0.9930666965431355,0.9963539512475978,0.9985576033916148,0.9876360004981617,0.7136233876651569,0.6280950833660081,0.5816501802009049,0.9235626743314267,94.0,5399.8,7.8,67.6
cat_boost,minmax_scaling,class_weight,0.05,6,3,1.0,10,5,0.9867844083028228,0.8573006791580667,0.8164907232214175,0.7944656991807557,0.958193575687438,0.9932316312539872,0.9964645201920334,0.9986315747283545,0.9878901498488986,0.7213697270621464,0.6365169262508016,0.5902998236331569,0.9284970015259774,95.4,5400.2,7.4,66.2
cat_boost,minmax_scaling,class_weight,0.05,8,1,0.8,10,5,0.98696397375443,0.8588520652710141,0.8174395366148681,0.7951607816902195,0.9620975495653582,0.9933240141817763,0.9965900576766396,0.9987795037224373,0.9879284071915372,0.7243801163602518,0.6382890155530964,0.5915420596580018,0.9362666919391796,95.6,5401.0,6.6,66.0
cat_boost,minmax_scaling,class_weight,0.05,8,1,1.0,10,5,0.9869639802020224,0.8589256487158436,0.8174769428455269,0.7951646191718738,0.962012871399407,0.9933239605530348,0.996590061243829,0.9987795105621355,0.9879281234018163,0.7245273368786525,0.6383638244472246,0.5915497277816119,0.9360976193969981,95.6,5401.0,6.6,66.0
cat_boost,minmax_scaling,class_weight,0.05,8,3,0.8,10,5,0.9866407494939446,0.8547901540994214,0.8131230269576963,0.7907913130218117,0.9595074852665129,0.993159153169854,0.9964795006647693,0.9987055460650941,0.9876746003357422,0.7164211550289888,0.6297665532506237,0.5828770799785292,0.9313403701972834,94.2,5400.6,7.0,67.4
cat_boost,minmax_scaling,class_weight,0.05,8,3,1.0,10,5,0.9868562344834656,0.8578940042542003,0.816713699641437,0.7945065189109306,0.9597825930249886,0.9932686639522277,0.996523643388767,0.9987055460650941,0.9878908442470614,0.722519344556173,0.6369037558941069,0.5903074917567671,0.9316743418029161,95.4,5400.6,7.0,66.2
cat_boost,minmax_scaling,class_weight,0.1,6,1,0.8,10,5,0.9870357548019252,0.8623889925070234,0.8248272214187775,0.8041988095438739,0.9520721366121677,0.9933574241810342,0.9963161878839678,0.9982987002931768,0.9884657552032878,0.7314205608330123,0.6533382549535874,0.610098918794571,0.9156785180210474,98.6,5398.4,9.2,63.0
cat_boost,minmax_scaling,class_weight,0.1,6,1,1.0,10,5,0.9871435005204819,0.8625998575702166,0.8236953790986726,0.8024579181169808,0.956632062491588,0.9934137922982996,0.9964715073780287,0.9985206211430941,0.9883597026951225,0.7317859228421337,0.6509192508193165,0.6063952150908672,0.9249044222880534,98.0,5399.6,8.0,63.6
cat_boost,minmax_scaling,class_weight,0.1,6,3,0.8,10,5,0.9873948857051393,0.8667458053044029,0.829664336892599,0.8091970780277107,0.954337080418209,0.9935407771781357,0.9964341832568916,0.9983726853093131,0.988756309486377,0.7399508334306704,0.6628944905283063,0.6200214707461085,0.919917851350041,100.2,5398.8,8.8,61.4
cat_boost,minmax_scaling,class_weight,0.1,6,3,1.0,10,5,0.9872153395963098,0.8644394608691248,0.8267940039700823,0.8060953220274041,0.9540443270536809,0.9934492769479496,0.9963973749809257,0.9983726853093129,0.9885750243912155,0.7354296447903,0.6571906329592391,0.613817958745495,0.9195136297161464,99.2,5398.8,8.8,62.4
cat_boost,minmax_scaling,class_weight,0.1,8,1,0.8,10,5,0.9868203084979591,0.8599023912397564,0.8223149533336459,0.8016926983325823,0.9497489390690376,0.9932473069538978,0.9962276995711379,0.9982247494755321,0.9883200574357269,0.7265574755256153,0.6484022070961538,0.6051606471896328,0.9111778207023482,97.8,5398.0,9.6,63.8
cat_boost,minmax_scaling,class_weight,0.1,8,1,1.0,10,5,0.9870357999350727,0.8618063547824375,0.8234090356320497,0.8023986072625455,0.9541161878278945,0.9933581830944259,0.9963828328350818,0.9984096675578338,0.988358275605339,0.7302545264704492,0.6504352384290177,0.6063875469672572,0.91987410005045,98.0,5399.0,8.6,63.6
cat_boost,minmax_scaling,class_weight,0.1,8,3,0.8,10,5,0.9870716936826167,0.8625515302503806,0.8245004500539862,0.8036300100271925,0.953530205956364,0.9933761441894067,0.9963679530945301,0.9983726647902182,0.988430069482742,0.7317269163113542,0.6526329470134422,0.6088873552641669,0.9186303424299856,98.4,5398.8,8.8,63.2
cat_boost,minmax_scaling,class_weight,0.1,8,3,1.0,10,5,0.9869998868447514,0.8607931667292659,0.8214250075291298,0.7999887788946625,0.9563493266635918,0.9933405747125796,0.9964420595578707,0.9985206143033958,0.9882145666495015,0.7282457587459524,0.646407955500389,0.6014569434859289,0.9244840866776822,97.2,5399.6,8.0,64.4
cat_boost,yeo_johnson,KMeansSMOTE,0.03,6,1,0.8,10,5,0.986102098272271,0.8464211332649116,0.8023135099790324,0.7791138815061434,0.96203288693187,0.9928862049080953,0.9964579165143898,0.9988534818988754,0.9869903534333956,0.699956061621728,0.6081691034436749,0.5593742811134115,0.9370754204303445,90.4,5401.4,6.2,71.2
cat_boost,yeo_johnson,KMeansSMOTE,0.03,6,1,1.0,10,5,0.9861020918246783,0.8469134138715712,0.803336559335327,0.7803076365169013,0.9596527280526976,0.9928857095450654,0.9964136002318558,0.9987795242415322,0.9870612190530006,0.7009411181980768,0.6102595184387981,0.5618357487922705,0.9322442370523942,90.8,5401.0,6.6,70.8
cat_boost,yeo_johnson,KMeansSMOTE,0.03,6,3,0.8,10,5,0.9859943590013067,0.8455895471055779,0.8020371696150306,0.7790660796768216,0.9589476503888168,0.9928306747692883,0.9963693077576995,0.9987425419930116,0.9869888604171744,0.6983484194418677,0.6077050314723615,0.5593896173606318,0.9309064403604594,90.4,5400.8,6.8,71.2
cat_boost,yeo_johnson,KMeansSMOTE,0.03,6,3,1.0,10,5,0.9861020982722708,0.8469259490456327,0.8033344354877547,0.7803038024550962,0.9598048908417075,0.9928856855497828,0.9964135841257609,0.998779524241532,0.9870612237987226,0.700966212541483,0.610255286849749,0.5618280806686603,0.9325485578846923,90.8,5401.0,6.6,70.8
cat_boost,yeo_johnson,KMeansSMOTE,0.03,8,1,0.8,10,5,0.9863893900996572,0.8500036099146012,0.8060615973468563,0.7828660730636322,0.964231805061976,0.9930328042996537,0.9965611507017205,0.9989274532356148,0.9872078427726576,0.7069744155295486,0.6155620439919921,0.5668046928916495,0.9412557673512941,91.6,5401.8,5.8,70.0
cat_boost,yeo_johnson,KMeansSMOTE,0.03,8,1,1.0,10,5,0.986245744185964,0.8476860128088646,0.8031711577107631,0.7797828081875858,0.9646225545136377,0.9929600993661,0.996553947866289,0.9989644354841356,0.9870279994042248,0.7024119262516294,0.6097883675552371,0.5606011808910359,0.9422171096230502,90.6,5402.0,5.6,71.0
cat_boost,yeo_johnson,KMeansSMOTE,0.03,8,3,0.8,10,5,0.9863893900996572,0.8495220361155831,0.8050537352020507,0.7816569886453523,0.9659996628309043,0.9930333414905947,0.9966055373221451,0.9990014245723546,0.9871366794500623,0.7060107307405714,0.6135019330819563,0.5643125527183498,0.944862646211746,91.2,5402.2,5.4,70.4
cat_boost,yeo_johnson,KMeansSMOTE,0.03,8,3,1.0,10,5,0.9863175639190143,0.8491915868997488,0.8053628818591857,0.7822264639269495,0.9631146491255429,0.9929960598572507,0.9965242639440666,0.9988904709870944,0.9871713330975938,0.7053871139422471,0.6142014997743052,0.5655624568668046,0.9390579651534919,91.4,5401.6,6.0,70.2
cat_boost,yeo_johnson,KMeansSMOTE,0.05,6,1,0.8,10,5,0.9866407688367221,0.8556837421302268,0.8150894007705028,0.7931979762531072,0.9563290102657941,0.99315811122897,0.9963906992292945,0.9985575965519166,0.987817299311019,0.7182093730314834,0.633788102311711,0.5878383559542979,0.9248407212205692,95.0,5399.8,7.8,66.6
cat_boost,yeo_johnson,KMeansSMOTE,0.05,6,1,1.0,10,5,0.9867484823173163,0.8559996996083085,0.8143727987698804,0.7920367073435457,0.960300275384561,0.9932142818892882,0.9965238018250784,0.9987425351533131,0.9877474074893051,0.7187851173273286,0.6322217957146827,0.585330879533778,0.9328531432798167,94.6,5400.8,6.8,67.0
cat_boost,yeo_johnson,KMeansSMOTE,0.05,6,3,0.8,10,5,0.9864612227278926,0.8533450421041462,0.812224517306803,0.7901038917962598,0.9555993374147942,0.9930666965431355,0.9963539512475978,0.9985576033916148,0.9876360004981617,0.7136233876651569,0.6280950833660081,0.5816501802009049,0.9235626743314267,94.0,5399.8,7.8,67.6
cat_boost,yeo_johnson,KMeansSMOTE,0.05,6,3,1.0,10,5,0.9867844083028228,0.8573006791580667,0.8164907232214175,0.7944656991807557,0.958193575687438,0.9932316312539872,0.9964645201920334,0.9986315747283545,0.9878901498488986,0.7213697270621464,0.6365169262508016,0.5902998236331569,0.9284970015259774,95.4,5400.2,7.4,66.2
cat_boost,yeo_johnson,KMeansSMOTE,0.05,8,1,0.8,10,5,0.98696397375443,0.8588520652710141,0.8174395366148681,0.7951607816902195,0.9620975495653582,0.9933240141817763,0.9965900576766396,0.9987795037224373,0.9879284071915372,0.7243801163602518,0.6382890155530964,0.5915420596580018,0.9362666919391796,95.6,5401.0,6.6,66.0
cat_boost,yeo_johnson,KMeansSMOTE,0.05,8,1,1.0,10,5,0.9869639802020224,0.8589256487158436,0.8174769428455269,0.7951646191718738,0.962012871399407,0.9933239605530348,0.996590061243829,0.9987795105621355,0.9879281234018163,0.7245273368786525,0.6383638244472246,0.5915497277816119,0.9360976193969981,95.6,5401.0,6.6,66.0
cat_boost,yeo_johnson,KMeansSMOTE,0.05,8,3,0.8,10,5,0.9866407494939446,0.8547901540994214,0.8131230269576963,0.7907913130218117,0.9595074852665129,0.993159153169854,0.9964795006647693,0.9987055460650941,0.9876746003357422,0.7164211550289888,0.6297665532506237,0.5828770799785292,0.9313403701972834,94.2,5400.6,7.0,67.4
cat_boost,yeo_johnson,KMeansSMOTE,0.05,8,3,1.0,10,5,0.9868562344834656,0.8578940042542003,0.816713699641437,0.7945065189109306,0.9597825930249886,0.9932686639522277,0.996523643388767,0.9987055460650941,0.9878908442470614,0.722519344556173,0.6369037558941069,0.5903074917567671,0.9316743418029161,95.4,5400.6,7.0,66.2
cat_boost,yeo_johnson,KMeansSMOTE,0.1,6,1,0.8,10,5,0.9870357548019252,0.8623889925070234,0.8248272214187775,0.8041988095438739,0.9520721366121677,0.9933574241810342,0.9963161878839678,0.9982987002931768,0.9884657552032878,0.7314205608330123,0.6533382549535874,0.610098918794571,0.9156785180210474,98.6,5398.4,9.2,63.0
cat_boost,yeo_johnson,KMeansSMOTE,0.1,6,1,1.0,10,5,0.9871435005204819,0.8625998575702166,0.8236953790986726,0.8024579181169808,0.956632062491588,0.9934137922982996,0.9964715073780287,0.9985206211430941,0.9883597026951225,0.7317859228421337,0.6509192508193165,0.6063952150908672,0.9249044222880534,98.0,5399.6,8.0,63.6
cat_boost,yeo_johnson,KMeansSMOTE,0.1,6,3,0.8,10,5,0.9873948857051393,0.8667458053044029,0.829664336892599,0.8091970780277107,0.954337080418209,0.9935407771781357,0.9964341832568916,0.9983726853093131,0.988756309486377,0.7399508334306704,0.6628944905283063,0.6200214707461085,0.919917851350041,100.2,5398.8,8.8,61.4
cat_boost,yeo_johnson,KMeansSMOTE,0.1,6,3,1.0,10,5,0.9872153395963098,0.8644394608691248,0.8267940039700823,0.8060953220274041,0.9540443270536809,0.9934492769479496,0.9963973749809257,0.9983726853093129,0.9885750243912155,0.7354296447903,0.6571906329592391,0.613817958745495,0.9195136297161464,99.2,5398.8,8.8,62.4
cat_boost,yeo_johnson,KMeansSMOTE,0.1,8,1,0.8,10,5,0.9868203084979591,0.8599023912397564,0.8223149533336459,0.8016926983325823,0.9497489390690376,0.9932473069538978,0.9962276995711379,0.9982247494755321,0.9883200574357269,0.7265574755256153,0.6484022070961538,0.6051606471896328,0.9111778207023482,97.8,5398.0,9.6,63.8
cat_boost,yeo_johnson,KMeansSMOTE,0.1,8,1,1.0,10,5,0.9870357999350727,0.8618063547824375,0.8234090356320497,0.8023986072625455,0.9541161878278945,0.9933581830944259,0.9963828328350818,0.9984096675578338,0.988358275605339,0.7302545264704492,0.6504352384290177,0.6063875469672572,0.91987410005045,98.0,5399.0,8.6,63.6
cat_boost,yeo_johnson,KMeansSMOTE,0.1,8,3,0.8,10,5,0.9870716936826167,0.8625515302503806,0.8245004500539862,0.8036300100271925,0.953530205956364,0.9933761441894067,0.9963679530945301,0.9983726647902182,0.988430069482742,0.7317269163113542,0.6526329470134422,0.6088873552641669,0.9186303424299856,98.4,5398.8,8.8,63.2
cat_boost,yeo_johnson,KMeansSMOTE,0.1,8,3,1.0,10,5,0.9869998868447514,0.8607931667292659,0.8214250075291298,0.7999887788946625,0.9563493266635918,0.9933405747125796,0.9964420595578707,0.9985206143033958,0.9882145666495015,0.7282457587459524,0.646407955500389,0.6014569434859289,0.9244840866776822,97.2,5399.6,8.0,64.4
cat_boost,yeo_johnson,class_weight,0.03,6,1,0.8,10,5,0.986102098272271,0.8464211332649116,0.8023135099790324,0.7791138815061434,0.96203288693187,0.9928862049080953,0.9964579165143898,0.9988534818988754,0.9869903534333956,0.699956061621728,0.6081691034436749,0.5593742811134115,0.9370754204303445,90.4,5401.4,6.2,71.2
cat_boost,yeo_johnson,class_weight,0.03,6,1,1.0,10,5,0.9861020918246783,0.8469134138715712,0.803336559335327,0.7803076365169013,0.9596527280526976,0.9928857095450654,0.9964136002318558,0.9987795242415322,0.9870612190530006,0.7009411181980768,0.6102595184387981,0.5618357487922705,0.9322442370523942,90.8,5401.0,6.6,70.8
cat_boost,yeo_johnson,class_weight,0.03,6,3,0.8,10,5,0.9859943590013067,0.8455895471055779,0.8020371696150306,0.7790660796768216,0.9589476503888168,0.9928306747692883,0.9963693077576995,0.9987425419930116,0.9869888604171744,0.6983484194418677,0.6077050314723615,0.5593896173606318,0.9309064403604594,90.4,5400.8,6.8,71.2
cat_boost,yeo_johnson,class_weight,0.03,6,3,1.0,10,5,0.9861020982722708,0.8469259490456327,0.8033344354877547,0.7803038024550962,0.9598048908417075,0.9928856855497828,0.9964135841257609,0.998779524241532,0.9870612237987226,0.700966212541483,0.610255286849749,0.5618280806686603,0.9325485578846923,90.8,5401.0,6.6,70.8
cat_boost,yeo_johnson,class_weight,0.03,8,1,0.8,10,5,0.9863893900996572,0.8500036099146012,0.8060615973468563,0.7828660730636322,0.964231805061976,0.9930328042996537,0.9965611507017205,0.9989274532356148,0.9872078427726576,0.7069744155295486,0.6155620439919921,0.5668046928916495,0.9412557673512941,91.6,5401.8,5.8,70.0
cat_boost,yeo_johnson,class_weight,0.03,8,1,1.0,10,5,0.986245744185964,0.8476860128088646,0.8031711577107631,0.7797828081875858,0.9646225545136377,0.9929600993661,0.996553947866289,0.9989644354841356,0.9870279994042248,0.7024119262516294,0.6097883675552371,0.5606011808910359,0.9422171096230502,90.6,5402.0,5.6,71.0
cat_boost,yeo_johnson,class_weight,0.03,8,3,0.8,10,5,0.9863893900996572,0.8495220361155831,0.8050537352020507,0.7816569886453523,0.9659996628309043,0.9930333414905947,0.9966055373221451,0.9990014245723546,0.9871366794500623,0.7060107307405714,0.6135019330819563,0.5643125527183498,0.944862646211746,91.2,5402.2,5.4,70.4
cat_boost,yeo_johnson,class_weight,0.03,8,3,1.0,10,5,0.9863175639190143,0.8491915868997488,0.8053628818591857,0.7822264639269495,0.9631146491255429,0.9929960598572507,0.9965242639440666,0.9988904709870944,0.9871713330975938,0.7053871139422471,0.6142014997743052,0.5655624568668046,0.9390579651534919,91.4,5401.6,6.0,70.2
cat_boost,yeo_johnson,class_weight,0.05,6,1,0.8,10,5,0.9866407688367221,0.8556837421302268,0.8150894007705028,0.7931979762531072,0.9563290102657941,0.99315811122897,0.9963906992292945,0.9985575965519166,0.987817299311019,0.7182093730314834,0.633788102311711,0.5878383559542979,0.9248407212205692,95.0,5399.8,7.8,66.6
cat_boost,yeo_johnson,class_weight,0.05,6,1,1.0,10,5,0.9867484823173163,0.8559996996083085,0.8143727987698804,0.7920367073435457,0.960300275384561,0.9932142818892882,0.9965238018250784,0.9987425351533131,0.9877474074893051,0.7187851173273286,0.6322217957146827,0.585330879533778,0.9328531432798167,94.6,5400.8,6.8,67.0
cat_boost,yeo_johnson,class_weight,0.05,6,3,0.8,10,5,0.9864612227278926,0.8533450421041462,0.812224517306803,0.7901038917962598,0.9555993374147942,0.9930666965431355,0.9963539512475978,0.9985576033916148,0.9876360004981617,0.7136233876651569,0.6280950833660081,0.5816501802009049,0.9235626743314267,94.0,5399.8,7.8,67.6
cat_boost,yeo_johnson,class_weight,0.05,6,3,1.0,10,5,0.9867844083028228,0.8573006791580667,0.8164907232214175,0.7944656991807557,0.958193575687438,0.9932316312539872,0.9964645201920334,0.9986315747283545,0.9878901498488986,0.7213697270621464,0.6365169262508016,0.5902998236331569,0.9284970015259774,95.4,5400.2,7.4,66.2
cat_boost,yeo_johnson,class_weight,0.05,8,1,0.8,10,5,0.98696397375443,0.8588520652710141,0.8174395366148681,0.7951607816902195,0.9620975495653582,0.9933240141817763,0.9965900576766396,0.9987795037224373,0.9879284071915372,0.7243801163602518,0.6382890155530964,0.5915420596580018,0.9362666919391796,95.6,5401.0,6.6,66.0
cat_boost,yeo_johnson,class_weight,0.05,8,1,1.0,10,5,0.9869639802020224,0.8589256487158436,0.8174769428455269,0.7951646191718738,0.962012871399407,0.9933239605530348,0.996590061243829,0.9987795105621355,0.9879281234018163,0.7245273368786525,0.6383638244472246,0.5915497277816119,0.9360976193969981,95.6,5401.0,6.6,66.0
cat_boost,yeo_johnson,class_weight,0.05,8,3,0.8,10,5,0.9866407494939446,0.8547901540994214,0.8131230269576963,0.7907913130218117,0.9595074852665129,0.993159153169854,0.9964795006647693,0.9987055460650941,0.9876746003357422,0.7164211550289888,0.6297665532506237,0.5828770799785292,0.9313403701972834,94.2,5400.6,7.0,67.4
cat_boost,yeo_johnson,class_weight,0.05,8,3,1.0,10,5,0.9868562344834656,0.8578940042542003,0.816713699641437,0.7945065189109306,0.9597825930249886,0.9932686639522277,0.996523643388767,0.9987055460650941,0.9878908442470614,0.722519344556173,0.6369037558941069,0.5903074917567671,0.9316743418029161,95.4,5400.6,7.0,66.2
cat_boost,yeo_johnson,class_weight,0.1,6,1,0.8,10,5,0.9870357548019252,0.8623889925070234,0.8248272214187775,0.8041988095438739,0.9520721366121677,0.9933574241810342,0.9963161878839678,0.9982987002931768,0.9884657552032878,0.7314205608330123,0.6533382549535874,0.610098918794571,0.9156785180210474,98.6,5398.4,9.2,63.0
cat_boost,yeo_johnson,class_weight,0.1,6,1,1.0,10,5,0.9871435005204819,0.8625998575702166,0.8236953790986726,0.8024579181169808,0.956632062491588,0.9934137922982996,0.9964715073780287,0.9985206211430941,0.9883597026951225,0.7317859228421337,0.6509192508193165,0.6063952150908672,0.9249044222880534,98.0,5399.6,8.0,63.6
cat_boost,yeo_johnson,class_weight,0.1,6,3,0.8,10,5,0.9873948857051393,0.8667458053044029,0.829664336892599,0.8091970780277107,0.954337080418209,0.9935407771781357,0.9964341832568916,0.9983726853093131,0.988756309486377,0.7399508334306704,0.6628944905283063,0.6200214707461085,0.919917851350041,100.2,5398.8,8.8,61.4
cat_boost,yeo_johnson,class_weight,0.1,6,3,1.0,10,5,0.9872153395963098,0.8644394608691248,0.8267940039700823,0.8060953220274041,0.9540443270536809,0.9934492769479496,0.9963973749809257,0.9983726853093129,0.9885750243912155,0.7354296447903,0.6571906329592391,0.613817958745495,0.9195136297161464,99.2,5398.8,8.8,62.4
cat_boost,yeo_johnson,class_weight,0.1,8,1,0.8,10,5,0.9868203084979591,0.8599023912397564,0.8223149533336459,0.8016926983325823,0.9497489390690376,0.9932473069538978,0.9962276995711379,0.9982247494755321,0.9883200574357269,0.7265574755256153,0.6484022070961538,0.6051606471896328,0.9111778207023482,97.8,5398.0,9.6,63.8
cat_boost,yeo_johnson,class_weight,0.1,8,1,1.0,10,5,0.9870357999350727,0.8618063547824375,0.8234090356320497,0.8023986072625455,0.9541161878278945,0.9933581830944259,0.9963828328350818,0.9984096675578338,0.988358275605339,0.7302545264704492,0.6504352384290177,0.6063875469672572,0.91987410005045,98.0,5399.0,8.6,63.6
cat_boost,yeo_johnson,class_weight,0.1,8,3,0.8,10,5,0.9870716936826167,0.8625515302503806,0.8245004500539862,0.8036300100271925,0.953530205956364,0.9933761441894067,0.9963679530945301,0.9983726647902182,0.988430069482742,0.7317269163113542,0.6526329470134422,0.6088873552641669,0.9186303424299856,98.4,5398.8,8.8,63.2
cat_boost,yeo_johnson,class_weight,0.1,8,3,1.0,10,5,0.9869998868447514,0.8607931667292659,0.8214250075291298,0.7999887788946625,0.9563493266635918,0.9933405747125796,0.9964420595578707,0.9985206143033958,0.9882145666495015,0.7282457587459524,0.646407955500389,0.6014569434859289,0.9244840866776822,97.2,5399.6,8.0,64.4
1 model scaling_method sampling_method learning_rate depth l2_leaf_reg subsample k_neighbors kmeans_estimator accuracy f1_macro f2_macro recall_macro precision_macro f1_class0 f2_class0 recall_class0 precision_class0 f1_class1 f2_class1 recall_class1 precision_class1 TP TN FP FN
2 cat_boost standard_scaling KMeansSMOTE 0.03 6 1 0.8 10 5 0.986102098272271 0.8464211332649116 0.8023135099790324 0.7791138815061434 0.96203288693187 0.9928862049080953 0.9964579165143898 0.9988534818988754 0.9869903534333956 0.699956061621728 0.6081691034436749 0.5593742811134115 0.9370754204303445 90.4 5401.4 6.2 71.2
3 cat_boost standard_scaling KMeansSMOTE 0.03 6 1 1.0 10 5 0.9861020918246783 0.8469134138715712 0.803336559335327 0.7803076365169013 0.9596527280526976 0.9928857095450654 0.9964136002318558 0.9987795242415322 0.9870612190530006 0.7009411181980768 0.6102595184387981 0.5618357487922705 0.9322442370523942 90.8 5401.0 6.6 70.8
4 cat_boost standard_scaling KMeansSMOTE 0.03 6 3 0.8 10 5 0.9859943590013067 0.8455895471055779 0.8020371696150306 0.7790660796768216 0.9589476503888168 0.9928306747692883 0.9963693077576995 0.9987425419930116 0.9869888604171744 0.6983484194418677 0.6077050314723615 0.5593896173606318 0.9309064403604594 90.4 5400.8 6.8 71.2
5 cat_boost standard_scaling KMeansSMOTE 0.03 6 3 1.0 10 5 0.9861020982722708 0.8469259490456327 0.8033344354877547 0.7803038024550962 0.9598048908417075 0.9928856855497828 0.9964135841257609 0.998779524241532 0.9870612237987226 0.700966212541483 0.610255286849749 0.5618280806686603 0.9325485578846923 90.8 5401.0 6.6 70.8
6 cat_boost standard_scaling KMeansSMOTE 0.03 8 1 0.8 10 5 0.9863893900996572 0.8500036099146012 0.8060615973468563 0.7828660730636322 0.964231805061976 0.9930328042996537 0.9965611507017205 0.9989274532356148 0.9872078427726576 0.7069744155295486 0.6155620439919921 0.5668046928916495 0.9412557673512941 91.6 5401.8 5.8 70.0
7 cat_boost standard_scaling KMeansSMOTE 0.03 8 1 1.0 10 5 0.986245744185964 0.8476860128088646 0.8031711577107631 0.7797828081875858 0.9646225545136377 0.9929600993661 0.996553947866289 0.9989644354841356 0.9870279994042248 0.7024119262516294 0.6097883675552371 0.5606011808910359 0.9422171096230502 90.6 5402.0 5.6 71.0
8 cat_boost standard_scaling KMeansSMOTE 0.03 8 3 0.8 10 5 0.9863893900996572 0.8495220361155831 0.8050537352020507 0.7816569886453523 0.9659996628309043 0.9930333414905947 0.9966055373221451 0.9990014245723546 0.9871366794500623 0.7060107307405714 0.6135019330819563 0.5643125527183498 0.944862646211746 91.2 5402.2 5.4 70.4
9 cat_boost standard_scaling KMeansSMOTE 0.03 8 3 1.0 10 5 0.9863175639190143 0.8491915868997488 0.8053628818591857 0.7822264639269495 0.9631146491255429 0.9929960598572507 0.9965242639440666 0.9988904709870944 0.9871713330975938 0.7053871139422471 0.6142014997743052 0.5655624568668046 0.9390579651534919 91.4 5401.6 6.0 70.2
10 cat_boost standard_scaling KMeansSMOTE 0.05 6 1 0.8 10 5 0.9866407688367221 0.8556837421302268 0.8150894007705028 0.7931979762531072 0.9563290102657941 0.99315811122897 0.9963906992292945 0.9985575965519166 0.987817299311019 0.7182093730314834 0.633788102311711 0.5878383559542979 0.9248407212205692 95.0 5399.8 7.8 66.6
11 cat_boost standard_scaling KMeansSMOTE 0.05 6 1 1.0 10 5 0.9867484823173163 0.8559996996083085 0.8143727987698804 0.7920367073435457 0.960300275384561 0.9932142818892882 0.9965238018250784 0.9987425351533131 0.9877474074893051 0.7187851173273286 0.6322217957146827 0.585330879533778 0.9328531432798167 94.6 5400.8 6.8 67.0
12 cat_boost standard_scaling KMeansSMOTE 0.05 6 3 0.8 10 5 0.9864612227278926 0.8533450421041462 0.812224517306803 0.7901038917962598 0.9555993374147942 0.9930666965431355 0.9963539512475978 0.9985576033916148 0.9876360004981617 0.7136233876651569 0.6280950833660081 0.5816501802009049 0.9235626743314267 94.0 5399.8 7.8 67.6
13 cat_boost standard_scaling KMeansSMOTE 0.05 6 3 1.0 10 5 0.9867844083028228 0.8573006791580667 0.8164907232214175 0.7944656991807557 0.958193575687438 0.9932316312539872 0.9964645201920334 0.9986315747283545 0.9878901498488986 0.7213697270621464 0.6365169262508016 0.5902998236331569 0.9284970015259774 95.4 5400.2 7.4 66.2
14 cat_boost standard_scaling KMeansSMOTE 0.05 8 1 0.8 10 5 0.98696397375443 0.8588520652710141 0.8174395366148681 0.7951607816902195 0.9620975495653582 0.9933240141817763 0.9965900576766396 0.9987795037224373 0.9879284071915372 0.7243801163602518 0.6382890155530964 0.5915420596580018 0.9362666919391796 95.6 5401.0 6.6 66.0
15 cat_boost standard_scaling KMeansSMOTE 0.05 8 1 1.0 10 5 0.9869639802020224 0.8589256487158436 0.8174769428455269 0.7951646191718738 0.962012871399407 0.9933239605530348 0.996590061243829 0.9987795105621355 0.9879281234018163 0.7245273368786525 0.6383638244472246 0.5915497277816119 0.9360976193969981 95.6 5401.0 6.6 66.0
16 cat_boost standard_scaling KMeansSMOTE 0.05 8 3 0.8 10 5 0.9866407494939446 0.8547901540994214 0.8131230269576963 0.7907913130218117 0.9595074852665129 0.993159153169854 0.9964795006647693 0.9987055460650941 0.9876746003357422 0.7164211550289888 0.6297665532506237 0.5828770799785292 0.9313403701972834 94.2 5400.6 7.0 67.4
17 cat_boost standard_scaling KMeansSMOTE 0.05 8 3 1.0 10 5 0.9868562344834656 0.8578940042542003 0.816713699641437 0.7945065189109306 0.9597825930249886 0.9932686639522277 0.996523643388767 0.9987055460650941 0.9878908442470614 0.722519344556173 0.6369037558941069 0.5903074917567671 0.9316743418029161 95.4 5400.6 7.0 66.2
18 cat_boost standard_scaling KMeansSMOTE 0.1 6 1 0.8 10 5 0.9870357548019252 0.8623889925070234 0.8248272214187775 0.8041988095438739 0.9520721366121677 0.9933574241810342 0.9963161878839678 0.9982987002931768 0.9884657552032878 0.7314205608330123 0.6533382549535874 0.610098918794571 0.9156785180210474 98.6 5398.4 9.2 63.0
19 cat_boost standard_scaling KMeansSMOTE 0.1 6 1 1.0 10 5 0.9871435005204819 0.8625998575702166 0.8236953790986726 0.8024579181169808 0.956632062491588 0.9934137922982996 0.9964715073780287 0.9985206211430941 0.9883597026951225 0.7317859228421337 0.6509192508193165 0.6063952150908672 0.9249044222880534 98.0 5399.6 8.0 63.6
20 cat_boost standard_scaling KMeansSMOTE 0.1 6 3 0.8 10 5 0.9873948857051393 0.8667458053044029 0.829664336892599 0.8091970780277107 0.954337080418209 0.9935407771781357 0.9964341832568916 0.9983726853093131 0.988756309486377 0.7399508334306704 0.6628944905283063 0.6200214707461085 0.919917851350041 100.2 5398.8 8.8 61.4
21 cat_boost standard_scaling KMeansSMOTE 0.1 6 3 1.0 10 5 0.9872153395963098 0.8644394608691248 0.8267940039700823 0.8060953220274041 0.9540443270536809 0.9934492769479496 0.9963973749809257 0.9983726853093129 0.9885750243912155 0.7354296447903 0.6571906329592391 0.613817958745495 0.9195136297161464 99.2 5398.8 8.8 62.4
22 cat_boost standard_scaling KMeansSMOTE 0.1 8 1 0.8 10 5 0.9868203084979591 0.8599023912397564 0.8223149533336459 0.8016926983325823 0.9497489390690376 0.9932473069538978 0.9962276995711379 0.9982247494755321 0.9883200574357269 0.7265574755256153 0.6484022070961538 0.6051606471896328 0.9111778207023482 97.8 5398.0 9.6 63.8
23 cat_boost standard_scaling KMeansSMOTE 0.1 8 1 1.0 10 5 0.9870357999350727 0.8618063547824375 0.8234090356320497 0.8023986072625455 0.9541161878278945 0.9933581830944259 0.9963828328350818 0.9984096675578338 0.988358275605339 0.7302545264704492 0.6504352384290177 0.6063875469672572 0.91987410005045 98.0 5399.0 8.6 63.6
24 cat_boost standard_scaling KMeansSMOTE 0.1 8 3 0.8 10 5 0.9870716936826167 0.8625515302503806 0.8245004500539862 0.8036300100271925 0.953530205956364 0.9933761441894067 0.9963679530945301 0.9983726647902182 0.988430069482742 0.7317269163113542 0.6526329470134422 0.6088873552641669 0.9186303424299856 98.4 5398.8 8.8 63.2
25 cat_boost standard_scaling KMeansSMOTE 0.1 8 3 1.0 10 5 0.9869998868447514 0.8607931667292659 0.8214250075291298 0.7999887788946625 0.9563493266635918 0.9933405747125796 0.9964420595578707 0.9985206143033958 0.9882145666495015 0.7282457587459524 0.646407955500389 0.6014569434859289 0.9244840866776822 97.2 5399.6 8.0 64.4
26 cat_boost standard_scaling class_weight 0.03 6 1 0.8 10 5 0.986102098272271 0.8464211332649116 0.8023135099790324 0.7791138815061434 0.96203288693187 0.9928862049080953 0.9964579165143898 0.9988534818988754 0.9869903534333956 0.699956061621728 0.6081691034436749 0.5593742811134115 0.9370754204303445 90.4 5401.4 6.2 71.2
27 cat_boost standard_scaling class_weight 0.03 6 1 1.0 10 5 0.9861020918246783 0.8469134138715712 0.803336559335327 0.7803076365169013 0.9596527280526976 0.9928857095450654 0.9964136002318558 0.9987795242415322 0.9870612190530006 0.7009411181980768 0.6102595184387981 0.5618357487922705 0.9322442370523942 90.8 5401.0 6.6 70.8
28 cat_boost standard_scaling class_weight 0.03 6 3 0.8 10 5 0.9859943590013067 0.8455895471055779 0.8020371696150306 0.7790660796768216 0.9589476503888168 0.9928306747692883 0.9963693077576995 0.9987425419930116 0.9869888604171744 0.6983484194418677 0.6077050314723615 0.5593896173606318 0.9309064403604594 90.4 5400.8 6.8 71.2
29 cat_boost standard_scaling class_weight 0.03 6 3 1.0 10 5 0.9861020982722708 0.8469259490456327 0.8033344354877547 0.7803038024550962 0.9598048908417075 0.9928856855497828 0.9964135841257609 0.998779524241532 0.9870612237987226 0.700966212541483 0.610255286849749 0.5618280806686603 0.9325485578846923 90.8 5401.0 6.6 70.8
30 cat_boost standard_scaling class_weight 0.03 8 1 0.8 10 5 0.9863893900996572 0.8500036099146012 0.8060615973468563 0.7828660730636322 0.964231805061976 0.9930328042996537 0.9965611507017205 0.9989274532356148 0.9872078427726576 0.7069744155295486 0.6155620439919921 0.5668046928916495 0.9412557673512941 91.6 5401.8 5.8 70.0
31 cat_boost standard_scaling class_weight 0.03 8 1 1.0 10 5 0.986245744185964 0.8476860128088646 0.8031711577107631 0.7797828081875858 0.9646225545136377 0.9929600993661 0.996553947866289 0.9989644354841356 0.9870279994042248 0.7024119262516294 0.6097883675552371 0.5606011808910359 0.9422171096230502 90.6 5402.0 5.6 71.0
32 cat_boost standard_scaling class_weight 0.03 8 3 0.8 10 5 0.9863893900996572 0.8495220361155831 0.8050537352020507 0.7816569886453523 0.9659996628309043 0.9930333414905947 0.9966055373221451 0.9990014245723546 0.9871366794500623 0.7060107307405714 0.6135019330819563 0.5643125527183498 0.944862646211746 91.2 5402.2 5.4 70.4
33 cat_boost standard_scaling class_weight 0.03 8 3 1.0 10 5 0.9863175639190143 0.8491915868997488 0.8053628818591857 0.7822264639269495 0.9631146491255429 0.9929960598572507 0.9965242639440666 0.9988904709870944 0.9871713330975938 0.7053871139422471 0.6142014997743052 0.5655624568668046 0.9390579651534919 91.4 5401.6 6.0 70.2
34 cat_boost standard_scaling class_weight 0.05 6 1 0.8 10 5 0.9866407688367221 0.8556837421302268 0.8150894007705028 0.7931979762531072 0.9563290102657941 0.99315811122897 0.9963906992292945 0.9985575965519166 0.987817299311019 0.7182093730314834 0.633788102311711 0.5878383559542979 0.9248407212205692 95.0 5399.8 7.8 66.6
35 cat_boost standard_scaling class_weight 0.05 6 1 1.0 10 5 0.9867484823173163 0.8559996996083085 0.8143727987698804 0.7920367073435457 0.960300275384561 0.9932142818892882 0.9965238018250784 0.9987425351533131 0.9877474074893051 0.7187851173273286 0.6322217957146827 0.585330879533778 0.9328531432798167 94.6 5400.8 6.8 67.0
36 cat_boost standard_scaling class_weight 0.05 6 3 0.8 10 5 0.9864612227278926 0.8533450421041462 0.812224517306803 0.7901038917962598 0.9555993374147942 0.9930666965431355 0.9963539512475978 0.9985576033916148 0.9876360004981617 0.7136233876651569 0.6280950833660081 0.5816501802009049 0.9235626743314267 94.0 5399.8 7.8 67.6
37 cat_boost standard_scaling class_weight 0.05 6 3 1.0 10 5 0.9867844083028228 0.8573006791580667 0.8164907232214175 0.7944656991807557 0.958193575687438 0.9932316312539872 0.9964645201920334 0.9986315747283545 0.9878901498488986 0.7213697270621464 0.6365169262508016 0.5902998236331569 0.9284970015259774 95.4 5400.2 7.4 66.2
38 cat_boost standard_scaling class_weight 0.05 8 1 0.8 10 5 0.98696397375443 0.8588520652710141 0.8174395366148681 0.7951607816902195 0.9620975495653582 0.9933240141817763 0.9965900576766396 0.9987795037224373 0.9879284071915372 0.7243801163602518 0.6382890155530964 0.5915420596580018 0.9362666919391796 95.6 5401.0 6.6 66.0
39 cat_boost standard_scaling class_weight 0.05 8 1 1.0 10 5 0.9869639802020224 0.8589256487158436 0.8174769428455269 0.7951646191718738 0.962012871399407 0.9933239605530348 0.996590061243829 0.9987795105621355 0.9879281234018163 0.7245273368786525 0.6383638244472246 0.5915497277816119 0.9360976193969981 95.6 5401.0 6.6 66.0
40 cat_boost standard_scaling class_weight 0.05 8 3 0.8 10 5 0.9866407494939446 0.8547901540994214 0.8131230269576963 0.7907913130218117 0.9595074852665129 0.993159153169854 0.9964795006647693 0.9987055460650941 0.9876746003357422 0.7164211550289888 0.6297665532506237 0.5828770799785292 0.9313403701972834 94.2 5400.6 7.0 67.4
41 cat_boost standard_scaling class_weight 0.05 8 3 1.0 10 5 0.9868562344834656 0.8578940042542003 0.816713699641437 0.7945065189109306 0.9597825930249886 0.9932686639522277 0.996523643388767 0.9987055460650941 0.9878908442470614 0.722519344556173 0.6369037558941069 0.5903074917567671 0.9316743418029161 95.4 5400.6 7.0 66.2
42 cat_boost standard_scaling class_weight 0.1 6 1 0.8 10 5 0.9870357548019252 0.8623889925070234 0.8248272214187775 0.8041988095438739 0.9520721366121677 0.9933574241810342 0.9963161878839678 0.9982987002931768 0.9884657552032878 0.7314205608330123 0.6533382549535874 0.610098918794571 0.9156785180210474 98.6 5398.4 9.2 63.0
43 cat_boost standard_scaling class_weight 0.1 6 1 1.0 10 5 0.9871435005204819 0.8625998575702166 0.8236953790986726 0.8024579181169808 0.956632062491588 0.9934137922982996 0.9964715073780287 0.9985206211430941 0.9883597026951225 0.7317859228421337 0.6509192508193165 0.6063952150908672 0.9249044222880534 98.0 5399.6 8.0 63.6
44 cat_boost standard_scaling class_weight 0.1 6 3 0.8 10 5 0.9873948857051393 0.8667458053044029 0.829664336892599 0.8091970780277107 0.954337080418209 0.9935407771781357 0.9964341832568916 0.9983726853093131 0.988756309486377 0.7399508334306704 0.6628944905283063 0.6200214707461085 0.919917851350041 100.2 5398.8 8.8 61.4
45 cat_boost standard_scaling class_weight 0.1 6 3 1.0 10 5 0.9872153395963098 0.8644394608691248 0.8267940039700823 0.8060953220274041 0.9540443270536809 0.9934492769479496 0.9963973749809257 0.9983726853093129 0.9885750243912155 0.7354296447903 0.6571906329592391 0.613817958745495 0.9195136297161464 99.2 5398.8 8.8 62.4
46 cat_boost standard_scaling class_weight 0.1 8 1 0.8 10 5 0.9868203084979591 0.8599023912397564 0.8223149533336459 0.8016926983325823 0.9497489390690376 0.9932473069538978 0.9962276995711379 0.9982247494755321 0.9883200574357269 0.7265574755256153 0.6484022070961538 0.6051606471896328 0.9111778207023482 97.8 5398.0 9.6 63.8
47 cat_boost standard_scaling class_weight 0.1 8 1 1.0 10 5 0.9870357999350727 0.8618063547824375 0.8234090356320497 0.8023986072625455 0.9541161878278945 0.9933581830944259 0.9963828328350818 0.9984096675578338 0.988358275605339 0.7302545264704492 0.6504352384290177 0.6063875469672572 0.91987410005045 98.0 5399.0 8.6 63.6
48 cat_boost standard_scaling class_weight 0.1 8 3 0.8 10 5 0.9870716936826167 0.8625515302503806 0.8245004500539862 0.8036300100271925 0.953530205956364 0.9933761441894067 0.9963679530945301 0.9983726647902182 0.988430069482742 0.7317269163113542 0.6526329470134422 0.6088873552641669 0.9186303424299856 98.4 5398.8 8.8 63.2
49 cat_boost standard_scaling class_weight 0.1 8 3 1.0 10 5 0.9869998868447514 0.8607931667292659 0.8214250075291298 0.7999887788946625 0.9563493266635918 0.9933405747125796 0.9964420595578707 0.9985206143033958 0.9882145666495015 0.7282457587459524 0.646407955500389 0.6014569434859289 0.9244840866776822 97.2 5399.6 8.0 64.4
50 cat_boost robust_scaling KMeansSMOTE 0.03 6 1 0.8 10 5 0.986102098272271 0.8464211332649116 0.8023135099790324 0.7791138815061434 0.96203288693187 0.9928862049080953 0.9964579165143898 0.9988534818988754 0.9869903534333956 0.699956061621728 0.6081691034436749 0.5593742811134115 0.9370754204303445 90.4 5401.4 6.2 71.2
51 cat_boost robust_scaling KMeansSMOTE 0.03 6 1 1.0 10 5 0.9861020918246783 0.8469134138715712 0.803336559335327 0.7803076365169013 0.9596527280526976 0.9928857095450654 0.9964136002318558 0.9987795242415322 0.9870612190530006 0.7009411181980768 0.6102595184387981 0.5618357487922705 0.9322442370523942 90.8 5401.0 6.6 70.8
52 cat_boost robust_scaling KMeansSMOTE 0.03 6 3 0.8 10 5 0.9859943590013067 0.8455895471055779 0.8020371696150306 0.7790660796768216 0.9589476503888168 0.9928306747692883 0.9963693077576995 0.9987425419930116 0.9869888604171744 0.6983484194418677 0.6077050314723615 0.5593896173606318 0.9309064403604594 90.4 5400.8 6.8 71.2
53 cat_boost robust_scaling KMeansSMOTE 0.03 6 3 1.0 10 5 0.9861020982722708 0.8469259490456327 0.8033344354877547 0.7803038024550962 0.9598048908417075 0.9928856855497828 0.9964135841257609 0.998779524241532 0.9870612237987226 0.700966212541483 0.610255286849749 0.5618280806686603 0.9325485578846923 90.8 5401.0 6.6 70.8
54 cat_boost robust_scaling KMeansSMOTE 0.03 8 1 0.8 10 5 0.9863893900996572 0.8500036099146012 0.8060615973468563 0.7828660730636322 0.964231805061976 0.9930328042996537 0.9965611507017205 0.9989274532356148 0.9872078427726576 0.7069744155295486 0.6155620439919921 0.5668046928916495 0.9412557673512941 91.6 5401.8 5.8 70.0
55 cat_boost robust_scaling KMeansSMOTE 0.03 8 1 1.0 10 5 0.986245744185964 0.8476860128088646 0.8031711577107631 0.7797828081875858 0.9646225545136377 0.9929600993661 0.996553947866289 0.9989644354841356 0.9870279994042248 0.7024119262516294 0.6097883675552371 0.5606011808910359 0.9422171096230502 90.6 5402.0 5.6 71.0
56 cat_boost robust_scaling KMeansSMOTE 0.03 8 3 0.8 10 5 0.9863893900996572 0.8495220361155831 0.8050537352020507 0.7816569886453523 0.9659996628309043 0.9930333414905947 0.9966055373221451 0.9990014245723546 0.9871366794500623 0.7060107307405714 0.6135019330819563 0.5643125527183498 0.944862646211746 91.2 5402.2 5.4 70.4
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64 cat_boost robust_scaling KMeansSMOTE 0.05 8 3 0.8 10 5 0.9866407494939446 0.8547901540994214 0.8131230269576963 0.7907913130218117 0.9595074852665129 0.993159153169854 0.9964795006647693 0.9987055460650941 0.9876746003357422 0.7164211550289888 0.6297665532506237 0.5828770799785292 0.9313403701972834 94.2 5400.6 7.0 67.4
65 cat_boost robust_scaling KMeansSMOTE 0.05 8 3 1.0 10 5 0.9868562344834656 0.8578940042542003 0.816713699641437 0.7945065189109306 0.9597825930249886 0.9932686639522277 0.996523643388767 0.9987055460650941 0.9878908442470614 0.722519344556173 0.6369037558941069 0.5903074917567671 0.9316743418029161 95.4 5400.6 7.0 66.2
66 cat_boost robust_scaling KMeansSMOTE 0.1 6 1 0.8 10 5 0.9870357548019252 0.8623889925070234 0.8248272214187775 0.8041988095438739 0.9520721366121677 0.9933574241810342 0.9963161878839678 0.9982987002931768 0.9884657552032878 0.7314205608330123 0.6533382549535874 0.610098918794571 0.9156785180210474 98.6 5398.4 9.2 63.0
67 cat_boost robust_scaling KMeansSMOTE 0.1 6 1 1.0 10 5 0.9871435005204819 0.8625998575702166 0.8236953790986726 0.8024579181169808 0.956632062491588 0.9934137922982996 0.9964715073780287 0.9985206211430941 0.9883597026951225 0.7317859228421337 0.6509192508193165 0.6063952150908672 0.9249044222880534 98.0 5399.6 8.0 63.6
68 cat_boost robust_scaling KMeansSMOTE 0.1 6 3 0.8 10 5 0.9873948857051393 0.8667458053044029 0.829664336892599 0.8091970780277107 0.954337080418209 0.9935407771781357 0.9964341832568916 0.9983726853093131 0.988756309486377 0.7399508334306704 0.6628944905283063 0.6200214707461085 0.919917851350041 100.2 5398.8 8.8 61.4
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71 cat_boost robust_scaling KMeansSMOTE 0.1 8 1 1.0 10 5 0.9870357999350727 0.8618063547824375 0.8234090356320497 0.8023986072625455 0.9541161878278945 0.9933581830944259 0.9963828328350818 0.9984096675578338 0.988358275605339 0.7302545264704492 0.6504352384290177 0.6063875469672572 0.91987410005045 98.0 5399.0 8.6 63.6
72 cat_boost robust_scaling KMeansSMOTE 0.1 8 3 0.8 10 5 0.9870716936826167 0.8625515302503806 0.8245004500539862 0.8036300100271925 0.953530205956364 0.9933761441894067 0.9963679530945301 0.9983726647902182 0.988430069482742 0.7317269163113542 0.6526329470134422 0.6088873552641669 0.9186303424299856 98.4 5398.8 8.8 63.2
73 cat_boost robust_scaling KMeansSMOTE 0.1 8 3 1.0 10 5 0.9869998868447514 0.8607931667292659 0.8214250075291298 0.7999887788946625 0.9563493266635918 0.9933405747125796 0.9964420595578707 0.9985206143033958 0.9882145666495015 0.7282457587459524 0.646407955500389 0.6014569434859289 0.9244840866776822 97.2 5399.6 8.0 64.4
74 cat_boost robust_scaling class_weight 0.03 6 1 0.8 10 5 0.986102098272271 0.8464211332649116 0.8023135099790324 0.7791138815061434 0.96203288693187 0.9928862049080953 0.9964579165143898 0.9988534818988754 0.9869903534333956 0.699956061621728 0.6081691034436749 0.5593742811134115 0.9370754204303445 90.4 5401.4 6.2 71.2
75 cat_boost robust_scaling class_weight 0.03 6 1 1.0 10 5 0.9861020918246783 0.8469134138715712 0.803336559335327 0.7803076365169013 0.9596527280526976 0.9928857095450654 0.9964136002318558 0.9987795242415322 0.9870612190530006 0.7009411181980768 0.6102595184387981 0.5618357487922705 0.9322442370523942 90.8 5401.0 6.6 70.8
76 cat_boost robust_scaling class_weight 0.03 6 3 0.8 10 5 0.9859943590013067 0.8455895471055779 0.8020371696150306 0.7790660796768216 0.9589476503888168 0.9928306747692883 0.9963693077576995 0.9987425419930116 0.9869888604171744 0.6983484194418677 0.6077050314723615 0.5593896173606318 0.9309064403604594 90.4 5400.8 6.8 71.2
77 cat_boost robust_scaling class_weight 0.03 6 3 1.0 10 5 0.9861020982722708 0.8469259490456327 0.8033344354877547 0.7803038024550962 0.9598048908417075 0.9928856855497828 0.9964135841257609 0.998779524241532 0.9870612237987226 0.700966212541483 0.610255286849749 0.5618280806686603 0.9325485578846923 90.8 5401.0 6.6 70.8
78 cat_boost robust_scaling class_weight 0.03 8 1 0.8 10 5 0.9863893900996572 0.8500036099146012 0.8060615973468563 0.7828660730636322 0.964231805061976 0.9930328042996537 0.9965611507017205 0.9989274532356148 0.9872078427726576 0.7069744155295486 0.6155620439919921 0.5668046928916495 0.9412557673512941 91.6 5401.8 5.8 70.0
79 cat_boost robust_scaling class_weight 0.03 8 1 1.0 10 5 0.986245744185964 0.8476860128088646 0.8031711577107631 0.7797828081875858 0.9646225545136377 0.9929600993661 0.996553947866289 0.9989644354841356 0.9870279994042248 0.7024119262516294 0.6097883675552371 0.5606011808910359 0.9422171096230502 90.6 5402.0 5.6 71.0
80 cat_boost robust_scaling class_weight 0.03 8 3 0.8 10 5 0.9863893900996572 0.8495220361155831 0.8050537352020507 0.7816569886453523 0.9659996628309043 0.9930333414905947 0.9966055373221451 0.9990014245723546 0.9871366794500623 0.7060107307405714 0.6135019330819563 0.5643125527183498 0.944862646211746 91.2 5402.2 5.4 70.4
81 cat_boost robust_scaling class_weight 0.03 8 3 1.0 10 5 0.9863175639190143 0.8491915868997488 0.8053628818591857 0.7822264639269495 0.9631146491255429 0.9929960598572507 0.9965242639440666 0.9988904709870944 0.9871713330975938 0.7053871139422471 0.6142014997743052 0.5655624568668046 0.9390579651534919 91.4 5401.6 6.0 70.2
82 cat_boost robust_scaling class_weight 0.05 6 1 0.8 10 5 0.9866407688367221 0.8556837421302268 0.8150894007705028 0.7931979762531072 0.9563290102657941 0.99315811122897 0.9963906992292945 0.9985575965519166 0.987817299311019 0.7182093730314834 0.633788102311711 0.5878383559542979 0.9248407212205692 95.0 5399.8 7.8 66.6
83 cat_boost robust_scaling class_weight 0.05 6 1 1.0 10 5 0.9867484823173163 0.8559996996083085 0.8143727987698804 0.7920367073435457 0.960300275384561 0.9932142818892882 0.9965238018250784 0.9987425351533131 0.9877474074893051 0.7187851173273286 0.6322217957146827 0.585330879533778 0.9328531432798167 94.6 5400.8 6.8 67.0
84 cat_boost robust_scaling class_weight 0.05 6 3 0.8 10 5 0.9864612227278926 0.8533450421041462 0.812224517306803 0.7901038917962598 0.9555993374147942 0.9930666965431355 0.9963539512475978 0.9985576033916148 0.9876360004981617 0.7136233876651569 0.6280950833660081 0.5816501802009049 0.9235626743314267 94.0 5399.8 7.8 67.6
85 cat_boost robust_scaling class_weight 0.05 6 3 1.0 10 5 0.9867844083028228 0.8573006791580667 0.8164907232214175 0.7944656991807557 0.958193575687438 0.9932316312539872 0.9964645201920334 0.9986315747283545 0.9878901498488986 0.7213697270621464 0.6365169262508016 0.5902998236331569 0.9284970015259774 95.4 5400.2 7.4 66.2
86 cat_boost robust_scaling class_weight 0.05 8 1 0.8 10 5 0.98696397375443 0.8588520652710141 0.8174395366148681 0.7951607816902195 0.9620975495653582 0.9933240141817763 0.9965900576766396 0.9987795037224373 0.9879284071915372 0.7243801163602518 0.6382890155530964 0.5915420596580018 0.9362666919391796 95.6 5401.0 6.6 66.0
87 cat_boost robust_scaling class_weight 0.05 8 1 1.0 10 5 0.9869639802020224 0.8589256487158436 0.8174769428455269 0.7951646191718738 0.962012871399407 0.9933239605530348 0.996590061243829 0.9987795105621355 0.9879281234018163 0.7245273368786525 0.6383638244472246 0.5915497277816119 0.9360976193969981 95.6 5401.0 6.6 66.0
88 cat_boost robust_scaling class_weight 0.05 8 3 0.8 10 5 0.9866407494939446 0.8547901540994214 0.8131230269576963 0.7907913130218117 0.9595074852665129 0.993159153169854 0.9964795006647693 0.9987055460650941 0.9876746003357422 0.7164211550289888 0.6297665532506237 0.5828770799785292 0.9313403701972834 94.2 5400.6 7.0 67.4
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90 cat_boost robust_scaling class_weight 0.1 6 1 0.8 10 5 0.9870357548019252 0.8623889925070234 0.8248272214187775 0.8041988095438739 0.9520721366121677 0.9933574241810342 0.9963161878839678 0.9982987002931768 0.9884657552032878 0.7314205608330123 0.6533382549535874 0.610098918794571 0.9156785180210474 98.6 5398.4 9.2 63.0
91 cat_boost robust_scaling class_weight 0.1 6 1 1.0 10 5 0.9871435005204819 0.8625998575702166 0.8236953790986726 0.8024579181169808 0.956632062491588 0.9934137922982996 0.9964715073780287 0.9985206211430941 0.9883597026951225 0.7317859228421337 0.6509192508193165 0.6063952150908672 0.9249044222880534 98.0 5399.6 8.0 63.6
92 cat_boost robust_scaling class_weight 0.1 6 3 0.8 10 5 0.9873948857051393 0.8667458053044029 0.829664336892599 0.8091970780277107 0.954337080418209 0.9935407771781357 0.9964341832568916 0.9983726853093131 0.988756309486377 0.7399508334306704 0.6628944905283063 0.6200214707461085 0.919917851350041 100.2 5398.8 8.8 61.4
93 cat_boost robust_scaling class_weight 0.1 6 3 1.0 10 5 0.9872153395963098 0.8644394608691248 0.8267940039700823 0.8060953220274041 0.9540443270536809 0.9934492769479496 0.9963973749809257 0.9983726853093129 0.9885750243912155 0.7354296447903 0.6571906329592391 0.613817958745495 0.9195136297161464 99.2 5398.8 8.8 62.4
94 cat_boost robust_scaling class_weight 0.1 8 1 0.8 10 5 0.9868203084979591 0.8599023912397564 0.8223149533336459 0.8016926983325823 0.9497489390690376 0.9932473069538978 0.9962276995711379 0.9982247494755321 0.9883200574357269 0.7265574755256153 0.6484022070961538 0.6051606471896328 0.9111778207023482 97.8 5398.0 9.6 63.8
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96 cat_boost robust_scaling class_weight 0.1 8 3 0.8 10 5 0.9870716936826167 0.8625515302503806 0.8245004500539862 0.8036300100271925 0.953530205956364 0.9933761441894067 0.9963679530945301 0.9983726647902182 0.988430069482742 0.7317269163113542 0.6526329470134422 0.6088873552641669 0.9186303424299856 98.4 5398.8 8.8 63.2
97 cat_boost robust_scaling class_weight 0.1 8 3 1.0 10 5 0.9869998868447514 0.8607931667292659 0.8214250075291298 0.7999887788946625 0.9563493266635918 0.9933405747125796 0.9964420595578707 0.9985206143033958 0.9882145666495015 0.7282457587459524 0.646407955500389 0.6014569434859289 0.9244840866776822 97.2 5399.6 8.0 64.4
98 cat_boost minmax_scaling KMeansSMOTE 0.03 6 1 0.8 10 5 0.986102098272271 0.8464211332649116 0.8023135099790324 0.7791138815061434 0.96203288693187 0.9928862049080953 0.9964579165143898 0.9988534818988754 0.9869903534333956 0.699956061621728 0.6081691034436749 0.5593742811134115 0.9370754204303445 90.4 5401.4 6.2 71.2
99 cat_boost minmax_scaling KMeansSMOTE 0.03 6 1 1.0 10 5 0.9861020918246783 0.8469134138715712 0.803336559335327 0.7803076365169013 0.9596527280526976 0.9928857095450654 0.9964136002318558 0.9987795242415322 0.9870612190530006 0.7009411181980768 0.6102595184387981 0.5618357487922705 0.9322442370523942 90.8 5401.0 6.6 70.8
100 cat_boost minmax_scaling KMeansSMOTE 0.03 6 3 0.8 10 5 0.9859943590013067 0.8455895471055779 0.8020371696150306 0.7790660796768216 0.9589476503888168 0.9928306747692883 0.9963693077576995 0.9987425419930116 0.9869888604171744 0.6983484194418677 0.6077050314723615 0.5593896173606318 0.9309064403604594 90.4 5400.8 6.8 71.2
101 cat_boost minmax_scaling KMeansSMOTE 0.03 6 3 1.0 10 5 0.9861020982722708 0.8469259490456327 0.8033344354877547 0.7803038024550962 0.9598048908417075 0.9928856855497828 0.9964135841257609 0.998779524241532 0.9870612237987226 0.700966212541483 0.610255286849749 0.5618280806686603 0.9325485578846923 90.8 5401.0 6.6 70.8
102 cat_boost minmax_scaling KMeansSMOTE 0.03 8 1 0.8 10 5 0.9863893900996572 0.8500036099146012 0.8060615973468563 0.7828660730636322 0.964231805061976 0.9930328042996537 0.9965611507017205 0.9989274532356148 0.9872078427726576 0.7069744155295486 0.6155620439919921 0.5668046928916495 0.9412557673512941 91.6 5401.8 5.8 70.0
103 cat_boost minmax_scaling KMeansSMOTE 0.03 8 1 1.0 10 5 0.986245744185964 0.8476860128088646 0.8031711577107631 0.7797828081875858 0.9646225545136377 0.9929600993661 0.996553947866289 0.9989644354841356 0.9870279994042248 0.7024119262516294 0.6097883675552371 0.5606011808910359 0.9422171096230502 90.6 5402.0 5.6 71.0
104 cat_boost minmax_scaling KMeansSMOTE 0.03 8 3 0.8 10 5 0.9863893900996572 0.8495220361155831 0.8050537352020507 0.7816569886453523 0.9659996628309043 0.9930333414905947 0.9966055373221451 0.9990014245723546 0.9871366794500623 0.7060107307405714 0.6135019330819563 0.5643125527183498 0.944862646211746 91.2 5402.2 5.4 70.4
105 cat_boost minmax_scaling KMeansSMOTE 0.03 8 3 1.0 10 5 0.9863175639190143 0.8491915868997488 0.8053628818591857 0.7822264639269495 0.9631146491255429 0.9929960598572507 0.9965242639440666 0.9988904709870944 0.9871713330975938 0.7053871139422471 0.6142014997743052 0.5655624568668046 0.9390579651534919 91.4 5401.6 6.0 70.2
106 cat_boost minmax_scaling KMeansSMOTE 0.05 6 1 0.8 10 5 0.9866407688367221 0.8556837421302268 0.8150894007705028 0.7931979762531072 0.9563290102657941 0.99315811122897 0.9963906992292945 0.9985575965519166 0.987817299311019 0.7182093730314834 0.633788102311711 0.5878383559542979 0.9248407212205692 95.0 5399.8 7.8 66.6
107 cat_boost minmax_scaling KMeansSMOTE 0.05 6 1 1.0 10 5 0.9867484823173163 0.8559996996083085 0.8143727987698804 0.7920367073435457 0.960300275384561 0.9932142818892882 0.9965238018250784 0.9987425351533131 0.9877474074893051 0.7187851173273286 0.6322217957146827 0.585330879533778 0.9328531432798167 94.6 5400.8 6.8 67.0
108 cat_boost minmax_scaling KMeansSMOTE 0.05 6 3 0.8 10 5 0.9864612227278926 0.8533450421041462 0.812224517306803 0.7901038917962598 0.9555993374147942 0.9930666965431355 0.9963539512475978 0.9985576033916148 0.9876360004981617 0.7136233876651569 0.6280950833660081 0.5816501802009049 0.9235626743314267 94.0 5399.8 7.8 67.6
109 cat_boost minmax_scaling KMeansSMOTE 0.05 6 3 1.0 10 5 0.9867844083028228 0.8573006791580667 0.8164907232214175 0.7944656991807557 0.958193575687438 0.9932316312539872 0.9964645201920334 0.9986315747283545 0.9878901498488986 0.7213697270621464 0.6365169262508016 0.5902998236331569 0.9284970015259774 95.4 5400.2 7.4 66.2
110 cat_boost minmax_scaling KMeansSMOTE 0.05 8 1 0.8 10 5 0.98696397375443 0.8588520652710141 0.8174395366148681 0.7951607816902195 0.9620975495653582 0.9933240141817763 0.9965900576766396 0.9987795037224373 0.9879284071915372 0.7243801163602518 0.6382890155530964 0.5915420596580018 0.9362666919391796 95.6 5401.0 6.6 66.0
111 cat_boost minmax_scaling KMeansSMOTE 0.05 8 1 1.0 10 5 0.9869639802020224 0.8589256487158436 0.8174769428455269 0.7951646191718738 0.962012871399407 0.9933239605530348 0.996590061243829 0.9987795105621355 0.9879281234018163 0.7245273368786525 0.6383638244472246 0.5915497277816119 0.9360976193969981 95.6 5401.0 6.6 66.0
112 cat_boost minmax_scaling KMeansSMOTE 0.05 8 3 0.8 10 5 0.9866407494939446 0.8547901540994214 0.8131230269576963 0.7907913130218117 0.9595074852665129 0.993159153169854 0.9964795006647693 0.9987055460650941 0.9876746003357422 0.7164211550289888 0.6297665532506237 0.5828770799785292 0.9313403701972834 94.2 5400.6 7.0 67.4
113 cat_boost minmax_scaling KMeansSMOTE 0.05 8 3 1.0 10 5 0.9868562344834656 0.8578940042542003 0.816713699641437 0.7945065189109306 0.9597825930249886 0.9932686639522277 0.996523643388767 0.9987055460650941 0.9878908442470614 0.722519344556173 0.6369037558941069 0.5903074917567671 0.9316743418029161 95.4 5400.6 7.0 66.2
114 cat_boost minmax_scaling KMeansSMOTE 0.1 6 1 0.8 10 5 0.9870357548019252 0.8623889925070234 0.8248272214187775 0.8041988095438739 0.9520721366121677 0.9933574241810342 0.9963161878839678 0.9982987002931768 0.9884657552032878 0.7314205608330123 0.6533382549535874 0.610098918794571 0.9156785180210474 98.6 5398.4 9.2 63.0
115 cat_boost minmax_scaling KMeansSMOTE 0.1 6 1 1.0 10 5 0.9871435005204819 0.8625998575702166 0.8236953790986726 0.8024579181169808 0.956632062491588 0.9934137922982996 0.9964715073780287 0.9985206211430941 0.9883597026951225 0.7317859228421337 0.6509192508193165 0.6063952150908672 0.9249044222880534 98.0 5399.6 8.0 63.6
116 cat_boost minmax_scaling KMeansSMOTE 0.1 6 3 0.8 10 5 0.9873948857051393 0.8667458053044029 0.829664336892599 0.8091970780277107 0.954337080418209 0.9935407771781357 0.9964341832568916 0.9983726853093131 0.988756309486377 0.7399508334306704 0.6628944905283063 0.6200214707461085 0.919917851350041 100.2 5398.8 8.8 61.4
117 cat_boost minmax_scaling KMeansSMOTE 0.1 6 3 1.0 10 5 0.9872153395963098 0.8644394608691248 0.8267940039700823 0.8060953220274041 0.9540443270536809 0.9934492769479496 0.9963973749809257 0.9983726853093129 0.9885750243912155 0.7354296447903 0.6571906329592391 0.613817958745495 0.9195136297161464 99.2 5398.8 8.8 62.4
118 cat_boost minmax_scaling KMeansSMOTE 0.1 8 1 0.8 10 5 0.9868203084979591 0.8599023912397564 0.8223149533336459 0.8016926983325823 0.9497489390690376 0.9932473069538978 0.9962276995711379 0.9982247494755321 0.9883200574357269 0.7265574755256153 0.6484022070961538 0.6051606471896328 0.9111778207023482 97.8 5398.0 9.6 63.8
119 cat_boost minmax_scaling KMeansSMOTE 0.1 8 1 1.0 10 5 0.9870357999350727 0.8618063547824375 0.8234090356320497 0.8023986072625455 0.9541161878278945 0.9933581830944259 0.9963828328350818 0.9984096675578338 0.988358275605339 0.7302545264704492 0.6504352384290177 0.6063875469672572 0.91987410005045 98.0 5399.0 8.6 63.6
120 cat_boost minmax_scaling KMeansSMOTE 0.1 8 3 0.8 10 5 0.9870716936826167 0.8625515302503806 0.8245004500539862 0.8036300100271925 0.953530205956364 0.9933761441894067 0.9963679530945301 0.9983726647902182 0.988430069482742 0.7317269163113542 0.6526329470134422 0.6088873552641669 0.9186303424299856 98.4 5398.8 8.8 63.2
121 cat_boost minmax_scaling KMeansSMOTE 0.1 8 3 1.0 10 5 0.9869998868447514 0.8607931667292659 0.8214250075291298 0.7999887788946625 0.9563493266635918 0.9933405747125796 0.9964420595578707 0.9985206143033958 0.9882145666495015 0.7282457587459524 0.646407955500389 0.6014569434859289 0.9244840866776822 97.2 5399.6 8.0 64.4
122 cat_boost minmax_scaling class_weight 0.03 6 1 0.8 10 5 0.986102098272271 0.8464211332649116 0.8023135099790324 0.7791138815061434 0.96203288693187 0.9928862049080953 0.9964579165143898 0.9988534818988754 0.9869903534333956 0.699956061621728 0.6081691034436749 0.5593742811134115 0.9370754204303445 90.4 5401.4 6.2 71.2
123 cat_boost minmax_scaling class_weight 0.03 6 1 1.0 10 5 0.9861020918246783 0.8469134138715712 0.803336559335327 0.7803076365169013 0.9596527280526976 0.9928857095450654 0.9964136002318558 0.9987795242415322 0.9870612190530006 0.7009411181980768 0.6102595184387981 0.5618357487922705 0.9322442370523942 90.8 5401.0 6.6 70.8
124 cat_boost minmax_scaling class_weight 0.03 6 3 0.8 10 5 0.9859943590013067 0.8455895471055779 0.8020371696150306 0.7790660796768216 0.9589476503888168 0.9928306747692883 0.9963693077576995 0.9987425419930116 0.9869888604171744 0.6983484194418677 0.6077050314723615 0.5593896173606318 0.9309064403604594 90.4 5400.8 6.8 71.2
125 cat_boost minmax_scaling class_weight 0.03 6 3 1.0 10 5 0.9861020982722708 0.8469259490456327 0.8033344354877547 0.7803038024550962 0.9598048908417075 0.9928856855497828 0.9964135841257609 0.998779524241532 0.9870612237987226 0.700966212541483 0.610255286849749 0.5618280806686603 0.9325485578846923 90.8 5401.0 6.6 70.8
126 cat_boost minmax_scaling class_weight 0.03 8 1 0.8 10 5 0.9863893900996572 0.8500036099146012 0.8060615973468563 0.7828660730636322 0.964231805061976 0.9930328042996537 0.9965611507017205 0.9989274532356148 0.9872078427726576 0.7069744155295486 0.6155620439919921 0.5668046928916495 0.9412557673512941 91.6 5401.8 5.8 70.0
127 cat_boost minmax_scaling class_weight 0.03 8 1 1.0 10 5 0.986245744185964 0.8476860128088646 0.8031711577107631 0.7797828081875858 0.9646225545136377 0.9929600993661 0.996553947866289 0.9989644354841356 0.9870279994042248 0.7024119262516294 0.6097883675552371 0.5606011808910359 0.9422171096230502 90.6 5402.0 5.6 71.0
128 cat_boost minmax_scaling class_weight 0.03 8 3 0.8 10 5 0.9863893900996572 0.8495220361155831 0.8050537352020507 0.7816569886453523 0.9659996628309043 0.9930333414905947 0.9966055373221451 0.9990014245723546 0.9871366794500623 0.7060107307405714 0.6135019330819563 0.5643125527183498 0.944862646211746 91.2 5402.2 5.4 70.4
129 cat_boost minmax_scaling class_weight 0.03 8 3 1.0 10 5 0.9863175639190143 0.8491915868997488 0.8053628818591857 0.7822264639269495 0.9631146491255429 0.9929960598572507 0.9965242639440666 0.9988904709870944 0.9871713330975938 0.7053871139422471 0.6142014997743052 0.5655624568668046 0.9390579651534919 91.4 5401.6 6.0 70.2
130 cat_boost minmax_scaling class_weight 0.05 6 1 0.8 10 5 0.9866407688367221 0.8556837421302268 0.8150894007705028 0.7931979762531072 0.9563290102657941 0.99315811122897 0.9963906992292945 0.9985575965519166 0.987817299311019 0.7182093730314834 0.633788102311711 0.5878383559542979 0.9248407212205692 95.0 5399.8 7.8 66.6
131 cat_boost minmax_scaling class_weight 0.05 6 1 1.0 10 5 0.9867484823173163 0.8559996996083085 0.8143727987698804 0.7920367073435457 0.960300275384561 0.9932142818892882 0.9965238018250784 0.9987425351533131 0.9877474074893051 0.7187851173273286 0.6322217957146827 0.585330879533778 0.9328531432798167 94.6 5400.8 6.8 67.0
132 cat_boost minmax_scaling class_weight 0.05 6 3 0.8 10 5 0.9864612227278926 0.8533450421041462 0.812224517306803 0.7901038917962598 0.9555993374147942 0.9930666965431355 0.9963539512475978 0.9985576033916148 0.9876360004981617 0.7136233876651569 0.6280950833660081 0.5816501802009049 0.9235626743314267 94.0 5399.8 7.8 67.6
133 cat_boost minmax_scaling class_weight 0.05 6 3 1.0 10 5 0.9867844083028228 0.8573006791580667 0.8164907232214175 0.7944656991807557 0.958193575687438 0.9932316312539872 0.9964645201920334 0.9986315747283545 0.9878901498488986 0.7213697270621464 0.6365169262508016 0.5902998236331569 0.9284970015259774 95.4 5400.2 7.4 66.2
134 cat_boost minmax_scaling class_weight 0.05 8 1 0.8 10 5 0.98696397375443 0.8588520652710141 0.8174395366148681 0.7951607816902195 0.9620975495653582 0.9933240141817763 0.9965900576766396 0.9987795037224373 0.9879284071915372 0.7243801163602518 0.6382890155530964 0.5915420596580018 0.9362666919391796 95.6 5401.0 6.6 66.0
135 cat_boost minmax_scaling class_weight 0.05 8 1 1.0 10 5 0.9869639802020224 0.8589256487158436 0.8174769428455269 0.7951646191718738 0.962012871399407 0.9933239605530348 0.996590061243829 0.9987795105621355 0.9879281234018163 0.7245273368786525 0.6383638244472246 0.5915497277816119 0.9360976193969981 95.6 5401.0 6.6 66.0
136 cat_boost minmax_scaling class_weight 0.05 8 3 0.8 10 5 0.9866407494939446 0.8547901540994214 0.8131230269576963 0.7907913130218117 0.9595074852665129 0.993159153169854 0.9964795006647693 0.9987055460650941 0.9876746003357422 0.7164211550289888 0.6297665532506237 0.5828770799785292 0.9313403701972834 94.2 5400.6 7.0 67.4
137 cat_boost minmax_scaling class_weight 0.05 8 3 1.0 10 5 0.9868562344834656 0.8578940042542003 0.816713699641437 0.7945065189109306 0.9597825930249886 0.9932686639522277 0.996523643388767 0.9987055460650941 0.9878908442470614 0.722519344556173 0.6369037558941069 0.5903074917567671 0.9316743418029161 95.4 5400.6 7.0 66.2
138 cat_boost minmax_scaling class_weight 0.1 6 1 0.8 10 5 0.9870357548019252 0.8623889925070234 0.8248272214187775 0.8041988095438739 0.9520721366121677 0.9933574241810342 0.9963161878839678 0.9982987002931768 0.9884657552032878 0.7314205608330123 0.6533382549535874 0.610098918794571 0.9156785180210474 98.6 5398.4 9.2 63.0
139 cat_boost minmax_scaling class_weight 0.1 6 1 1.0 10 5 0.9871435005204819 0.8625998575702166 0.8236953790986726 0.8024579181169808 0.956632062491588 0.9934137922982996 0.9964715073780287 0.9985206211430941 0.9883597026951225 0.7317859228421337 0.6509192508193165 0.6063952150908672 0.9249044222880534 98.0 5399.6 8.0 63.6
140 cat_boost minmax_scaling class_weight 0.1 6 3 0.8 10 5 0.9873948857051393 0.8667458053044029 0.829664336892599 0.8091970780277107 0.954337080418209 0.9935407771781357 0.9964341832568916 0.9983726853093131 0.988756309486377 0.7399508334306704 0.6628944905283063 0.6200214707461085 0.919917851350041 100.2 5398.8 8.8 61.4
141 cat_boost minmax_scaling class_weight 0.1 6 3 1.0 10 5 0.9872153395963098 0.8644394608691248 0.8267940039700823 0.8060953220274041 0.9540443270536809 0.9934492769479496 0.9963973749809257 0.9983726853093129 0.9885750243912155 0.7354296447903 0.6571906329592391 0.613817958745495 0.9195136297161464 99.2 5398.8 8.8 62.4
142 cat_boost minmax_scaling class_weight 0.1 8 1 0.8 10 5 0.9868203084979591 0.8599023912397564 0.8223149533336459 0.8016926983325823 0.9497489390690376 0.9932473069538978 0.9962276995711379 0.9982247494755321 0.9883200574357269 0.7265574755256153 0.6484022070961538 0.6051606471896328 0.9111778207023482 97.8 5398.0 9.6 63.8
143 cat_boost minmax_scaling class_weight 0.1 8 1 1.0 10 5 0.9870357999350727 0.8618063547824375 0.8234090356320497 0.8023986072625455 0.9541161878278945 0.9933581830944259 0.9963828328350818 0.9984096675578338 0.988358275605339 0.7302545264704492 0.6504352384290177 0.6063875469672572 0.91987410005045 98.0 5399.0 8.6 63.6
144 cat_boost minmax_scaling class_weight 0.1 8 3 0.8 10 5 0.9870716936826167 0.8625515302503806 0.8245004500539862 0.8036300100271925 0.953530205956364 0.9933761441894067 0.9963679530945301 0.9983726647902182 0.988430069482742 0.7317269163113542 0.6526329470134422 0.6088873552641669 0.9186303424299856 98.4 5398.8 8.8 63.2
145 cat_boost minmax_scaling class_weight 0.1 8 3 1.0 10 5 0.9869998868447514 0.8607931667292659 0.8214250075291298 0.7999887788946625 0.9563493266635918 0.9933405747125796 0.9964420595578707 0.9985206143033958 0.9882145666495015 0.7282457587459524 0.646407955500389 0.6014569434859289 0.9244840866776822 97.2 5399.6 8.0 64.4
146 cat_boost yeo_johnson KMeansSMOTE 0.03 6 1 0.8 10 5 0.986102098272271 0.8464211332649116 0.8023135099790324 0.7791138815061434 0.96203288693187 0.9928862049080953 0.9964579165143898 0.9988534818988754 0.9869903534333956 0.699956061621728 0.6081691034436749 0.5593742811134115 0.9370754204303445 90.4 5401.4 6.2 71.2
147 cat_boost yeo_johnson KMeansSMOTE 0.03 6 1 1.0 10 5 0.9861020918246783 0.8469134138715712 0.803336559335327 0.7803076365169013 0.9596527280526976 0.9928857095450654 0.9964136002318558 0.9987795242415322 0.9870612190530006 0.7009411181980768 0.6102595184387981 0.5618357487922705 0.9322442370523942 90.8 5401.0 6.6 70.8
148 cat_boost yeo_johnson KMeansSMOTE 0.03 6 3 0.8 10 5 0.9859943590013067 0.8455895471055779 0.8020371696150306 0.7790660796768216 0.9589476503888168 0.9928306747692883 0.9963693077576995 0.9987425419930116 0.9869888604171744 0.6983484194418677 0.6077050314723615 0.5593896173606318 0.9309064403604594 90.4 5400.8 6.8 71.2
149 cat_boost yeo_johnson KMeansSMOTE 0.03 6 3 1.0 10 5 0.9861020982722708 0.8469259490456327 0.8033344354877547 0.7803038024550962 0.9598048908417075 0.9928856855497828 0.9964135841257609 0.998779524241532 0.9870612237987226 0.700966212541483 0.610255286849749 0.5618280806686603 0.9325485578846923 90.8 5401.0 6.6 70.8
150 cat_boost yeo_johnson KMeansSMOTE 0.03 8 1 0.8 10 5 0.9863893900996572 0.8500036099146012 0.8060615973468563 0.7828660730636322 0.964231805061976 0.9930328042996537 0.9965611507017205 0.9989274532356148 0.9872078427726576 0.7069744155295486 0.6155620439919921 0.5668046928916495 0.9412557673512941 91.6 5401.8 5.8 70.0
151 cat_boost yeo_johnson KMeansSMOTE 0.03 8 1 1.0 10 5 0.986245744185964 0.8476860128088646 0.8031711577107631 0.7797828081875858 0.9646225545136377 0.9929600993661 0.996553947866289 0.9989644354841356 0.9870279994042248 0.7024119262516294 0.6097883675552371 0.5606011808910359 0.9422171096230502 90.6 5402.0 5.6 71.0
152 cat_boost yeo_johnson KMeansSMOTE 0.03 8 3 0.8 10 5 0.9863893900996572 0.8495220361155831 0.8050537352020507 0.7816569886453523 0.9659996628309043 0.9930333414905947 0.9966055373221451 0.9990014245723546 0.9871366794500623 0.7060107307405714 0.6135019330819563 0.5643125527183498 0.944862646211746 91.2 5402.2 5.4 70.4
153 cat_boost yeo_johnson KMeansSMOTE 0.03 8 3 1.0 10 5 0.9863175639190143 0.8491915868997488 0.8053628818591857 0.7822264639269495 0.9631146491255429 0.9929960598572507 0.9965242639440666 0.9988904709870944 0.9871713330975938 0.7053871139422471 0.6142014997743052 0.5655624568668046 0.9390579651534919 91.4 5401.6 6.0 70.2
154 cat_boost yeo_johnson KMeansSMOTE 0.05 6 1 0.8 10 5 0.9866407688367221 0.8556837421302268 0.8150894007705028 0.7931979762531072 0.9563290102657941 0.99315811122897 0.9963906992292945 0.9985575965519166 0.987817299311019 0.7182093730314834 0.633788102311711 0.5878383559542979 0.9248407212205692 95.0 5399.8 7.8 66.6
155 cat_boost yeo_johnson KMeansSMOTE 0.05 6 1 1.0 10 5 0.9867484823173163 0.8559996996083085 0.8143727987698804 0.7920367073435457 0.960300275384561 0.9932142818892882 0.9965238018250784 0.9987425351533131 0.9877474074893051 0.7187851173273286 0.6322217957146827 0.585330879533778 0.9328531432798167 94.6 5400.8 6.8 67.0
156 cat_boost yeo_johnson KMeansSMOTE 0.05 6 3 0.8 10 5 0.9864612227278926 0.8533450421041462 0.812224517306803 0.7901038917962598 0.9555993374147942 0.9930666965431355 0.9963539512475978 0.9985576033916148 0.9876360004981617 0.7136233876651569 0.6280950833660081 0.5816501802009049 0.9235626743314267 94.0 5399.8 7.8 67.6
157 cat_boost yeo_johnson KMeansSMOTE 0.05 6 3 1.0 10 5 0.9867844083028228 0.8573006791580667 0.8164907232214175 0.7944656991807557 0.958193575687438 0.9932316312539872 0.9964645201920334 0.9986315747283545 0.9878901498488986 0.7213697270621464 0.6365169262508016 0.5902998236331569 0.9284970015259774 95.4 5400.2 7.4 66.2
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iter Passed Remaining
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3
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model,accuracy,f1_macro,f2_macro,recall_macro,precision_macro,f1_class0,f2_class0,recall_class0,precision_class0,f1_class1,f2_class1,recall_class1,precision_class1,TP,TN,FP,FN
catboost,0.9814814814814815,0.8195693865042805,0.8013174756506312,0.7903526990720451,0.8559205703525894,0.9904901243599122,0.9921698350221925,0.9932928107315029,0.9877032096706961,0.6486486486486487,0.6104651162790697,0.5874125874125874,0.7241379310344828,84,4739,32,59
lightgbm,0.9849409849409849,0.8469442386692707,0.8185917013944679,0.8023094072140393,0.9084632979829487,0.9922755741127348,0.9946427824048885,0.9962272060364703,0.9883551673944687,0.7016129032258065,0.6425406203840472,0.6083916083916084,0.8285714285714286,87,4753,18,56
1 model accuracy f1_macro f2_macro recall_macro precision_macro f1_class0 f2_class0 recall_class0 precision_class0 f1_class1 f2_class1 recall_class1 precision_class1 TP TN FP FN
2 catboost 0.9814814814814815 0.8195693865042805 0.8013174756506312 0.7903526990720451 0.8559205703525894 0.9904901243599122 0.9921698350221925 0.9932928107315029 0.9877032096706961 0.6486486486486487 0.6104651162790697 0.5874125874125874 0.7241379310344828 84 4739 32 59
3 lightgbm 0.9849409849409849 0.8469442386692707 0.8185917013944679 0.8023094072140393 0.9084632979829487 0.9922755741127348 0.9946427824048885 0.9962272060364703 0.9883551673944687 0.7016129032258065 0.6425406203840472 0.6083916083916084 0.8285714285714286 87 4753 18 56

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lightgbm,standard_scaling,KMeansSMOTE,gbdt,0.03,100,0.1,0.1,0.8,1.0,10,5,0.9873948857051393,0.8660654376430875,0.8278053873421346,0.8067827329934085,0.9571174874451384,0.9935416656406844,0.9965229981920896,0.9985206074636975,0.9886125885446072,0.7385892096454906,0.6590877764921795,0.6150448585231194,0.9256223863456696,99.4,5399.6,8.0,62.2
lightgbm,standard_scaling,KMeansSMOTE,gbdt,0.03,100,0.1,0.1,1.0,0.8,10,5,0.9872871270913975,0.8636064344844797,0.8240482741313839,0.8025088748233425,0.9596889613355953,0.9934880088638021,0.9965897765183312,0.9986685432974788,0.9883616207181698,0.7337248601051574,0.6515067717444367,0.6063492063492064,0.9310163019530208,98.0,5400.4,7.2,63.6
lightgbm,standard_scaling,KMeansSMOTE,gbdt,0.03,100,0.1,0.1,1.0,1.0,10,5,0.9871434811777045,0.863449771888091,0.8256341947711338,0.8048454076194018,0.9533828520533423,0.9934127773860718,0.9963827135574705,0.9983726647902182,0.9885024551041862,0.7334867663901103,0.6548856759847967,0.6113181504485852,0.9182632490024984,98.8,5398.8,8.8,62.8
lightgbm,standard_scaling,KMeansSMOTE,gbdt,0.05,100,0.1,0.1,0.8,0.8,10,5,0.987358998405188,0.8658212328853709,0.8277045950376559,0.8067604078073429,0.9566247977036533,0.9935230811207543,0.9964934015324072,0.998483625215177,0.9886121814446716,0.7381193846499876,0.6589157885429044,0.6150371903995093,0.9246374139626351,99.4,5399.4,8.2,62.2
lightgbm,standard_scaling,KMeansSMOTE,gbdt,0.05,100,0.1,0.1,0.8,1.0,10,5,0.9876462902325743,0.8702280820945317,0.8337605692544805,0.8135218826445911,0.9553624954113376,0.9936685848798849,0.9964855981899191,0.9983726511108216,0.9890093876458101,0.7467875793091783,0.6710355403190421,0.6286711141783605,0.9217156031768651,101.6,5398.8,8.8,60.0
lightgbm,standard_scaling,KMeansSMOTE,gbdt,0.05,100,0.1,0.1,1.0,0.8,10,5,0.9876103706946605,0.8685306532899453,0.8303217288431941,0.8092965157481593,0.9592309417836985,0.9936518407305781,0.9966115244112512,0.9985945651210407,0.9887583083957047,0.7434094658493124,0.6640319332751371,0.619998466375278,0.9297035751716924,100.2,5400.0,7.6,61.4
lightgbm,standard_scaling,KMeansSMOTE,gbdt,0.05,100,0.1,0.1,1.0,1.0,10,5,0.9875744640519315,0.8697383859570031,0.8335824387321773,0.8134887344578754,0.9538702717550708,0.993631447606049,0.9964264483135749,0.9982986866137802,0.9890083881850659,0.7458453243079577,0.6707384291507799,0.6286787823019708,0.9187321553250758,101.6,5398.4,9.2,60.0
lightgbm,standard_scaling,KMeansSMOTE,gbdt,0.1,100,0.1,0.1,0.8,0.8,10,5,0.9876463031277595,0.8696452935291772,0.832345622530247,0.8117216735235644,0.9573224840422384,0.9936693371232203,0.99655223796351,0.9984836046960821,0.988901862104868,0.7456212499351342,0.6681390070969839,0.6249597423510467,0.9257431059796088,101.0,5399.4,8.2,60.6
lightgbm,standard_scaling,KMeansSMOTE,gbdt,0.1,100,0.1,0.1,0.8,1.0,10,5,0.9879335756123682,0.8735142470990855,0.8373885067575266,0.81727407420208,0.9572776440521376,0.9938154842661611,0.9965889028545231,0.9984466224475614,0.9892278794141068,0.7532130099320097,0.6781881106605302,0.6361015259565985,0.9253274086901682,102.8,5399.2,8.4,58.8
lightgbm,standard_scaling,KMeansSMOTE,gbdt,0.1,100,0.1,0.1,1.0,0.8,10,5,0.9874667118857822,0.8680481539924493,0.8317553825774301,0.8116330485442184,0.9529694195282257,0.993576898041232,0.9964044843956639,0.9982986866137802,0.9889004742854578,0.7425194099436665,0.6671062807591965,0.6249674104746569,0.9170383647709939,101.0,5398.4,9.2,60.6
lightgbm,standard_scaling,KMeansSMOTE,gbdt,0.1,100,0.1,0.1,1.0,1.0,10,5,0.9879694887026897,0.8749776173022346,0.840353596310616,0.8209121572972682,0.9537927663883565,0.9938325369626163,0.9964852212462812,0.9982617043652595,0.9894432107974284,0.7561226976418527,0.6842219713749508,0.6435626102292769,0.9181423219792844,104.0,5398.2,9.4,57.6
lightgbm,standard_scaling,KMeansSMOTE,dart,0.03,100,0.1,0.1,0.8,0.8,10,5,0.9864612162803,0.8517213882755159,0.8087404085309796,0.785885510418824,0.9614784852134713,0.9930685455041811,0.9965093140147973,0.9988164928106563,0.9873866482719492,0.7103742310468509,0.620971503047162,0.5729545280269918,0.9355703221549934,92.6,5401.2,6.4,69.0
lightgbm,standard_scaling,KMeansSMOTE,dart,0.03,100,0.1,0.1,0.8,1.0,10,5,0.9862816572762855,0.8494531720877854,0.8062811698729515,0.7834055481958557,0.9607205770272069,0.9929769287184236,0.9964502905368763,0.9987795037224373,0.9872419067787573,0.7059294154571474,0.6161120492090266,0.5680315926692738,0.9341992472756562,91.8,5401.0,6.6,69.8
lightgbm,standard_scaling,KMeansSMOTE,dart,0.03,100,0.1,0.1,1.0,0.8,10,5,0.9868203342883293,0.8567123113396862,0.81462313212734,0.7920698521104123,0.9624609919498163,0.993251161582376,0.9965828464697711,0.9988164928106563,0.9877478834902378,0.7201734610969963,0.632663417784909,0.585323211410168,0.9371741004093945,94.6,5401.2,6.4,67.0
lightgbm,standard_scaling,KMeansSMOTE,dart,0.03,100,0.1,0.1,1.0,1.0,10,5,0.9863893900996572,0.8516614690849792,0.8095557057072785,0.7870576057488851,0.9579981706050438,0.9930309459196085,0.9964058116257544,0.9986685432974787,0.9874568744274971,0.7102919922503499,0.6227055997888027,0.5754466682002913,0.9285394667825905,93.0,5400.4,7.2,68.6
lightgbm,standard_scaling,KMeansSMOTE,dart,0.05,100,0.1,0.1,0.8,0.8,10,5,0.9872871464341749,0.8647825564498515,0.8265211036628628,0.8055143377206931,0.9560208920519685,0.9934866215661462,0.9964787449584944,0.998483625215177,0.9885398629294059,0.7360784913335567,0.6565634623672312,0.6125450502262096,0.9235019211745309,99.0,5399.4,8.2,62.6
lightgbm,standard_scaling,KMeansSMOTE,dart,0.05,100,0.1,0.1,0.8,1.0,10,5,0.9872512526866313,0.864242117798965,0.8255296821802522,0.8043135971909393,0.9568082528265623,0.9934684617996755,0.9964935591422892,0.9985206074636975,0.9884674795460324,0.7350157737982544,0.6545658052182155,0.6101065869181811,0.9251490261070924,98.6,5399.6,8.0,63.0
lightgbm,standard_scaling,KMeansSMOTE,dart,0.05,100,0.1,0.1,1.0,0.8,10,5,0.9875385574092025,0.867739957700573,0.8292362302142348,0.808058120624818,0.9590571825576781,0.9936151348160316,0.9965968036054408,0.9985945788004372,0.9886852713014047,0.7418647805851146,0.6618756568230287,0.6175216624491988,0.9294290938139514,99.8,5400.0,7.6,61.8
lightgbm,standard_scaling,KMeansSMOTE,dart,0.05,100,0.1,0.1,1.0,1.0,10,5,0.9878617429841328,0.8718918805916948,0.8343953837040834,0.8136405111931599,0.9597257588431093,0.9937798152798208,0.9966630340635951,0.998594571960739,0.9890116522948971,0.7500039459035688,0.6721277333445717,0.6286864504255809,0.9304398653913216,101.6,5400.0,7.6,60.0
lightgbm,standard_scaling,KMeansSMOTE,dart,0.1,100,0.1,0.1,0.8,0.8,10,5,0.9875744511567464,0.8694904253032434,0.8330898747073772,0.8128784428659526,0.9543165483350643,0.9936317630222906,0.9964486981215537,0.9983356757019992,0.9889726061606664,0.7453490875841962,0.6697310512932007,0.6274212100299057,0.9196604905094622,101.4,5398.6,9.0,60.2
lightgbm,standard_scaling,KMeansSMOTE,dart,0.1,100,0.1,0.1,0.8,1.0,10,5,0.9877540230559461,0.8705274306318904,0.8326760658152557,0.8117771571558929,0.9596652466682235,0.993724889952756,0.9966409360538494,0.998594571960739,0.9889028548898798,0.7473299713110246,0.6687111955766621,0.6249597423510467,0.930427638446567,101.0,5400.0,7.6,60.6
lightgbm,standard_scaling,KMeansSMOTE,dart,0.1,100,0.1,0.1,1.0,0.8,10,5,0.9878976496268617,0.8740424916234224,0.8391944377539111,0.819654574765656,0.9536373604366759,0.9937959382660164,0.9964704965030846,0.9982616838461646,0.9893706349659366,0.7542890449808285,0.681918379004738,0.6410474656851469,0.9179040859074152,103.6,5398.2,9.4,58.0
lightgbm,standard_scaling,KMeansSMOTE,dart,0.1,100,0.1,0.1,1.0,1.0,10,5,0.98804128264537,0.8754763636546178,0.8405284491350876,0.8209184704711975,0.9551236805467838,0.9938696715557122,0.9965444000780066,0.9983356757019992,0.9894439864987652,0.7570830557535235,0.6845124981921686,0.6435012652403957,0.9208033745948025,104.0,5398.6,9.0,57.6
lightgbm,standard_scaling,class_weight,gbdt,0.03,100,0.1,0.1,0.8,0.8,10,5,0.9872871528817676,0.8635827226233982,0.82363889290808,0.8019139194786398,0.9607516165514827,0.9934880771322151,0.9966119285054977,0.9987055323856977,0.9883253364382266,0.7336773681145814,0.6506658573106621,0.605122306571582,0.9331778966647388,97.8,5400.6,7.0,63.8
lightgbm,standard_scaling,class_weight,gbdt,0.03,100,0.1,0.1,0.8,1.0,10,5,0.9873948857051393,0.8660654376430875,0.8278053873421346,0.8067827329934085,0.9571174874451384,0.9935416656406844,0.9965229981920896,0.9985206074636975,0.9886125885446072,0.7385892096454906,0.6590877764921795,0.6150448585231194,0.9256223863456696,99.4,5399.6,8.0,62.2
lightgbm,standard_scaling,class_weight,gbdt,0.03,100,0.1,0.1,1.0,0.8,10,5,0.9872871270913975,0.8636064344844797,0.8240482741313839,0.8025088748233425,0.9596889613355953,0.9934880088638021,0.9965897765183312,0.9986685432974788,0.9883616207181698,0.7337248601051574,0.6515067717444367,0.6063492063492064,0.9310163019530208,98.0,5400.4,7.2,63.6
lightgbm,standard_scaling,class_weight,gbdt,0.03,100,0.1,0.1,1.0,1.0,10,5,0.9871434811777045,0.863449771888091,0.8256341947711338,0.8048454076194018,0.9533828520533423,0.9934127773860718,0.9963827135574705,0.9983726647902182,0.9885024551041862,0.7334867663901103,0.6548856759847967,0.6113181504485852,0.9182632490024984,98.8,5398.8,8.8,62.8
lightgbm,standard_scaling,class_weight,gbdt,0.05,100,0.1,0.1,0.8,0.8,10,5,0.987358998405188,0.8658212328853709,0.8277045950376559,0.8067604078073429,0.9566247977036533,0.9935230811207543,0.9964934015324072,0.998483625215177,0.9886121814446716,0.7381193846499876,0.6589157885429044,0.6150371903995093,0.9246374139626351,99.4,5399.4,8.2,62.2
lightgbm,standard_scaling,class_weight,gbdt,0.05,100,0.1,0.1,0.8,1.0,10,5,0.9876462902325743,0.8702280820945317,0.8337605692544805,0.8135218826445911,0.9553624954113376,0.9936685848798849,0.9964855981899191,0.9983726511108216,0.9890093876458101,0.7467875793091783,0.6710355403190421,0.6286711141783605,0.9217156031768651,101.6,5398.8,8.8,60.0
lightgbm,standard_scaling,class_weight,gbdt,0.05,100,0.1,0.1,1.0,0.8,10,5,0.9876103706946605,0.8685306532899453,0.8303217288431941,0.8092965157481593,0.9592309417836985,0.9936518407305781,0.9966115244112512,0.9985945651210407,0.9887583083957047,0.7434094658493124,0.6640319332751371,0.619998466375278,0.9297035751716924,100.2,5400.0,7.6,61.4
lightgbm,standard_scaling,class_weight,gbdt,0.05,100,0.1,0.1,1.0,1.0,10,5,0.9875744640519315,0.8697383859570031,0.8335824387321773,0.8134887344578754,0.9538702717550708,0.993631447606049,0.9964264483135749,0.9982986866137802,0.9890083881850659,0.7458453243079577,0.6707384291507799,0.6286787823019708,0.9187321553250758,101.6,5398.4,9.2,60.0
lightgbm,standard_scaling,class_weight,gbdt,0.1,100,0.1,0.1,0.8,0.8,10,5,0.9876463031277595,0.8696452935291772,0.832345622530247,0.8117216735235644,0.9573224840422384,0.9936693371232203,0.99655223796351,0.9984836046960821,0.988901862104868,0.7456212499351342,0.6681390070969839,0.6249597423510467,0.9257431059796088,101.0,5399.4,8.2,60.6
lightgbm,standard_scaling,class_weight,gbdt,0.1,100,0.1,0.1,0.8,1.0,10,5,0.9879335756123682,0.8735142470990855,0.8373885067575266,0.81727407420208,0.9572776440521376,0.9938154842661611,0.9965889028545231,0.9984466224475614,0.9892278794141068,0.7532130099320097,0.6781881106605302,0.6361015259565985,0.9253274086901682,102.8,5399.2,8.4,58.8
lightgbm,standard_scaling,class_weight,gbdt,0.1,100,0.1,0.1,1.0,0.8,10,5,0.9874667118857822,0.8680481539924493,0.8317553825774301,0.8116330485442184,0.9529694195282257,0.993576898041232,0.9964044843956639,0.9982986866137802,0.9889004742854578,0.7425194099436665,0.6671062807591965,0.6249674104746569,0.9170383647709939,101.0,5398.4,9.2,60.6
lightgbm,standard_scaling,class_weight,gbdt,0.1,100,0.1,0.1,1.0,1.0,10,5,0.9879694887026897,0.8749776173022346,0.840353596310616,0.8209121572972682,0.9537927663883565,0.9938325369626163,0.9964852212462812,0.9982617043652595,0.9894432107974284,0.7561226976418527,0.6842219713749508,0.6435626102292769,0.9181423219792844,104.0,5398.2,9.4,57.6
lightgbm,standard_scaling,class_weight,dart,0.03,100,0.1,0.1,0.8,0.8,10,5,0.9864612162803,0.8517213882755159,0.8087404085309796,0.785885510418824,0.9614784852134713,0.9930685455041811,0.9965093140147973,0.9988164928106563,0.9873866482719492,0.7103742310468509,0.620971503047162,0.5729545280269918,0.9355703221549934,92.6,5401.2,6.4,69.0
lightgbm,standard_scaling,class_weight,dart,0.03,100,0.1,0.1,0.8,1.0,10,5,0.9862816572762855,0.8494531720877854,0.8062811698729515,0.7834055481958557,0.9607205770272069,0.9929769287184236,0.9964502905368763,0.9987795037224373,0.9872419067787573,0.7059294154571474,0.6161120492090266,0.5680315926692738,0.9341992472756562,91.8,5401.0,6.6,69.8
lightgbm,standard_scaling,class_weight,dart,0.03,100,0.1,0.1,1.0,0.8,10,5,0.9868203342883293,0.8567123113396862,0.81462313212734,0.7920698521104123,0.9624609919498163,0.993251161582376,0.9965828464697711,0.9988164928106563,0.9877478834902378,0.7201734610969963,0.632663417784909,0.585323211410168,0.9371741004093945,94.6,5401.2,6.4,67.0
lightgbm,standard_scaling,class_weight,dart,0.03,100,0.1,0.1,1.0,1.0,10,5,0.9863893900996572,0.8516614690849792,0.8095557057072785,0.7870576057488851,0.9579981706050438,0.9930309459196085,0.9964058116257544,0.9986685432974787,0.9874568744274971,0.7102919922503499,0.6227055997888027,0.5754466682002913,0.9285394667825905,93.0,5400.4,7.2,68.6
lightgbm,standard_scaling,class_weight,dart,0.05,100,0.1,0.1,0.8,0.8,10,5,0.9872871464341749,0.8647825564498515,0.8265211036628628,0.8055143377206931,0.9560208920519685,0.9934866215661462,0.9964787449584944,0.998483625215177,0.9885398629294059,0.7360784913335567,0.6565634623672312,0.6125450502262096,0.9235019211745309,99.0,5399.4,8.2,62.6
lightgbm,standard_scaling,class_weight,dart,0.05,100,0.1,0.1,0.8,1.0,10,5,0.9872512526866313,0.864242117798965,0.8255296821802522,0.8043135971909393,0.9568082528265623,0.9934684617996755,0.9964935591422892,0.9985206074636975,0.9884674795460324,0.7350157737982544,0.6545658052182155,0.6101065869181811,0.9251490261070924,98.6,5399.6,8.0,63.0
lightgbm,standard_scaling,class_weight,dart,0.05,100,0.1,0.1,1.0,0.8,10,5,0.9875385574092025,0.867739957700573,0.8292362302142348,0.808058120624818,0.9590571825576781,0.9936151348160316,0.9965968036054408,0.9985945788004372,0.9886852713014047,0.7418647805851146,0.6618756568230287,0.6175216624491988,0.9294290938139514,99.8,5400.0,7.6,61.8
lightgbm,standard_scaling,class_weight,dart,0.05,100,0.1,0.1,1.0,1.0,10,5,0.9878617429841328,0.8718918805916948,0.8343953837040834,0.8136405111931599,0.9597257588431093,0.9937798152798208,0.9966630340635951,0.998594571960739,0.9890116522948971,0.7500039459035688,0.6721277333445717,0.6286864504255809,0.9304398653913216,101.6,5400.0,7.6,60.0
lightgbm,standard_scaling,class_weight,dart,0.1,100,0.1,0.1,0.8,0.8,10,5,0.9875744511567464,0.8694904253032434,0.8330898747073772,0.8128784428659526,0.9543165483350643,0.9936317630222906,0.9964486981215537,0.9983356757019992,0.9889726061606664,0.7453490875841962,0.6697310512932007,0.6274212100299057,0.9196604905094622,101.4,5398.6,9.0,60.2
lightgbm,standard_scaling,class_weight,dart,0.1,100,0.1,0.1,0.8,1.0,10,5,0.9877540230559461,0.8705274306318904,0.8326760658152557,0.8117771571558929,0.9596652466682235,0.993724889952756,0.9966409360538494,0.998594571960739,0.9889028548898798,0.7473299713110246,0.6687111955766621,0.6249597423510467,0.930427638446567,101.0,5400.0,7.6,60.6
lightgbm,standard_scaling,class_weight,dart,0.1,100,0.1,0.1,1.0,0.8,10,5,0.9878976496268617,0.8740424916234224,0.8391944377539111,0.819654574765656,0.9536373604366759,0.9937959382660164,0.9964704965030846,0.9982616838461646,0.9893706349659366,0.7542890449808285,0.681918379004738,0.6410474656851469,0.9179040859074152,103.6,5398.2,9.4,58.0
lightgbm,standard_scaling,class_weight,dart,0.1,100,0.1,0.1,1.0,1.0,10,5,0.98804128264537,0.8754763636546178,0.8405284491350876,0.8209184704711975,0.9551236805467838,0.9938696715557122,0.9965444000780066,0.9983356757019992,0.9894439864987652,0.7570830557535235,0.6845124981921686,0.6435012652403957,0.9208033745948025,104.0,5398.6,9.0,57.6
lightgbm,robust_scaling,KMeansSMOTE,gbdt,0.03,100,0.1,0.1,0.8,0.8,10,5,0.9872871528817676,0.8635827226233982,0.82363889290808,0.8019139194786398,0.9607516165514827,0.9934880771322151,0.9966119285054977,0.9987055323856977,0.9883253364382266,0.7336773681145814,0.6506658573106621,0.605122306571582,0.9331778966647388,97.8,5400.6,7.0,63.8
lightgbm,robust_scaling,KMeansSMOTE,gbdt,0.03,100,0.1,0.1,0.8,1.0,10,5,0.9873948857051393,0.8660654376430875,0.8278053873421346,0.8067827329934085,0.9571174874451384,0.9935416656406844,0.9965229981920896,0.9985206074636975,0.9886125885446072,0.7385892096454906,0.6590877764921795,0.6150448585231194,0.9256223863456696,99.4,5399.6,8.0,62.2
lightgbm,robust_scaling,KMeansSMOTE,gbdt,0.03,100,0.1,0.1,1.0,0.8,10,5,0.9872871270913975,0.8636064344844797,0.8240482741313839,0.8025088748233425,0.9596889613355953,0.9934880088638021,0.9965897765183312,0.9986685432974788,0.9883616207181698,0.7337248601051574,0.6515067717444367,0.6063492063492064,0.9310163019530208,98.0,5400.4,7.2,63.6
lightgbm,robust_scaling,KMeansSMOTE,gbdt,0.03,100,0.1,0.1,1.0,1.0,10,5,0.9871434811777045,0.863449771888091,0.8256341947711338,0.8048454076194018,0.9533828520533423,0.9934127773860718,0.9963827135574705,0.9983726647902182,0.9885024551041862,0.7334867663901103,0.6548856759847967,0.6113181504485852,0.9182632490024984,98.8,5398.8,8.8,62.8
lightgbm,robust_scaling,KMeansSMOTE,gbdt,0.05,100,0.1,0.1,0.8,0.8,10,5,0.987358998405188,0.8658212328853709,0.8277045950376559,0.8067604078073429,0.9566247977036533,0.9935230811207543,0.9964934015324072,0.998483625215177,0.9886121814446716,0.7381193846499876,0.6589157885429044,0.6150371903995093,0.9246374139626351,99.4,5399.4,8.2,62.2
lightgbm,robust_scaling,KMeansSMOTE,gbdt,0.05,100,0.1,0.1,0.8,1.0,10,5,0.9876462902325743,0.8702280820945317,0.8337605692544805,0.8135218826445911,0.9553624954113376,0.9936685848798849,0.9964855981899191,0.9983726511108216,0.9890093876458101,0.7467875793091783,0.6710355403190421,0.6286711141783605,0.9217156031768651,101.6,5398.8,8.8,60.0
lightgbm,robust_scaling,KMeansSMOTE,gbdt,0.05,100,0.1,0.1,1.0,0.8,10,5,0.9876103706946605,0.8685306532899453,0.8303217288431941,0.8092965157481593,0.9592309417836985,0.9936518407305781,0.9966115244112512,0.9985945651210407,0.9887583083957047,0.7434094658493124,0.6640319332751371,0.619998466375278,0.9297035751716924,100.2,5400.0,7.6,61.4
lightgbm,robust_scaling,KMeansSMOTE,gbdt,0.05,100,0.1,0.1,1.0,1.0,10,5,0.9875744640519315,0.8697383859570031,0.8335824387321773,0.8134887344578754,0.9538702717550708,0.993631447606049,0.9964264483135749,0.9982986866137802,0.9890083881850659,0.7458453243079577,0.6707384291507799,0.6286787823019708,0.9187321553250758,101.6,5398.4,9.2,60.0
lightgbm,robust_scaling,KMeansSMOTE,gbdt,0.1,100,0.1,0.1,0.8,0.8,10,5,0.9876463031277595,0.8696452935291772,0.832345622530247,0.8117216735235644,0.9573224840422384,0.9936693371232203,0.99655223796351,0.9984836046960821,0.988901862104868,0.7456212499351342,0.6681390070969839,0.6249597423510467,0.9257431059796088,101.0,5399.4,8.2,60.6
lightgbm,robust_scaling,KMeansSMOTE,gbdt,0.1,100,0.1,0.1,0.8,1.0,10,5,0.9879335756123682,0.8735142470990855,0.8373885067575266,0.81727407420208,0.9572776440521376,0.9938154842661611,0.9965889028545231,0.9984466224475614,0.9892278794141068,0.7532130099320097,0.6781881106605302,0.6361015259565985,0.9253274086901682,102.8,5399.2,8.4,58.8
lightgbm,robust_scaling,KMeansSMOTE,gbdt,0.1,100,0.1,0.1,1.0,0.8,10,5,0.9874667118857822,0.8680481539924493,0.8317553825774301,0.8116330485442184,0.9529694195282257,0.993576898041232,0.9964044843956639,0.9982986866137802,0.9889004742854578,0.7425194099436665,0.6671062807591965,0.6249674104746569,0.9170383647709939,101.0,5398.4,9.2,60.6
lightgbm,robust_scaling,KMeansSMOTE,gbdt,0.1,100,0.1,0.1,1.0,1.0,10,5,0.9879694887026897,0.8749776173022346,0.840353596310616,0.8209121572972682,0.9537927663883565,0.9938325369626163,0.9964852212462812,0.9982617043652595,0.9894432107974284,0.7561226976418527,0.6842219713749508,0.6435626102292769,0.9181423219792844,104.0,5398.2,9.4,57.6
lightgbm,robust_scaling,KMeansSMOTE,dart,0.03,100,0.1,0.1,0.8,0.8,10,5,0.9864612162803,0.8517213882755159,0.8087404085309796,0.785885510418824,0.9614784852134713,0.9930685455041811,0.9965093140147973,0.9988164928106563,0.9873866482719492,0.7103742310468509,0.620971503047162,0.5729545280269918,0.9355703221549934,92.6,5401.2,6.4,69.0
lightgbm,robust_scaling,KMeansSMOTE,dart,0.03,100,0.1,0.1,0.8,1.0,10,5,0.9862816572762855,0.8494531720877854,0.8062811698729515,0.7834055481958557,0.9607205770272069,0.9929769287184236,0.9964502905368763,0.9987795037224373,0.9872419067787573,0.7059294154571474,0.6161120492090266,0.5680315926692738,0.9341992472756562,91.8,5401.0,6.6,69.8
lightgbm,robust_scaling,KMeansSMOTE,dart,0.03,100,0.1,0.1,1.0,0.8,10,5,0.9868203342883293,0.8567123113396862,0.81462313212734,0.7920698521104123,0.9624609919498163,0.993251161582376,0.9965828464697711,0.9988164928106563,0.9877478834902378,0.7201734610969963,0.632663417784909,0.585323211410168,0.9371741004093945,94.6,5401.2,6.4,67.0
lightgbm,robust_scaling,KMeansSMOTE,dart,0.03,100,0.1,0.1,1.0,1.0,10,5,0.9863893900996572,0.8516614690849792,0.8095557057072785,0.7870576057488851,0.9579981706050438,0.9930309459196085,0.9964058116257544,0.9986685432974787,0.9874568744274971,0.7102919922503499,0.6227055997888027,0.5754466682002913,0.9285394667825905,93.0,5400.4,7.2,68.6
lightgbm,robust_scaling,KMeansSMOTE,dart,0.05,100,0.1,0.1,0.8,0.8,10,5,0.9872871464341749,0.8647825564498515,0.8265211036628628,0.8055143377206931,0.9560208920519685,0.9934866215661462,0.9964787449584944,0.998483625215177,0.9885398629294059,0.7360784913335567,0.6565634623672312,0.6125450502262096,0.9235019211745309,99.0,5399.4,8.2,62.6
lightgbm,robust_scaling,KMeansSMOTE,dart,0.05,100,0.1,0.1,0.8,1.0,10,5,0.9872512526866313,0.864242117798965,0.8255296821802522,0.8043135971909393,0.9568082528265623,0.9934684617996755,0.9964935591422892,0.9985206074636975,0.9884674795460324,0.7350157737982544,0.6545658052182155,0.6101065869181811,0.9251490261070924,98.6,5399.6,8.0,63.0
lightgbm,robust_scaling,KMeansSMOTE,dart,0.05,100,0.1,0.1,1.0,0.8,10,5,0.9875385574092025,0.867739957700573,0.8292362302142348,0.808058120624818,0.9590571825576781,0.9936151348160316,0.9965968036054408,0.9985945788004372,0.9886852713014047,0.7418647805851146,0.6618756568230287,0.6175216624491988,0.9294290938139514,99.8,5400.0,7.6,61.8
lightgbm,robust_scaling,KMeansSMOTE,dart,0.05,100,0.1,0.1,1.0,1.0,10,5,0.9878617429841328,0.8718918805916948,0.8343953837040834,0.8136405111931599,0.9597257588431093,0.9937798152798208,0.9966630340635951,0.998594571960739,0.9890116522948971,0.7500039459035688,0.6721277333445717,0.6286864504255809,0.9304398653913216,101.6,5400.0,7.6,60.0
lightgbm,robust_scaling,KMeansSMOTE,dart,0.1,100,0.1,0.1,0.8,0.8,10,5,0.9875744511567464,0.8694904253032434,0.8330898747073772,0.8128784428659526,0.9543165483350643,0.9936317630222906,0.9964486981215537,0.9983356757019992,0.9889726061606664,0.7453490875841962,0.6697310512932007,0.6274212100299057,0.9196604905094622,101.4,5398.6,9.0,60.2
lightgbm,robust_scaling,KMeansSMOTE,dart,0.1,100,0.1,0.1,0.8,1.0,10,5,0.9877540230559461,0.8705274306318904,0.8326760658152557,0.8117771571558929,0.9596652466682235,0.993724889952756,0.9966409360538494,0.998594571960739,0.9889028548898798,0.7473299713110246,0.6687111955766621,0.6249597423510467,0.930427638446567,101.0,5400.0,7.6,60.6
lightgbm,robust_scaling,KMeansSMOTE,dart,0.1,100,0.1,0.1,1.0,0.8,10,5,0.9878976496268617,0.8740424916234224,0.8391944377539111,0.819654574765656,0.9536373604366759,0.9937959382660164,0.9964704965030846,0.9982616838461646,0.9893706349659366,0.7542890449808285,0.681918379004738,0.6410474656851469,0.9179040859074152,103.6,5398.2,9.4,58.0
lightgbm,robust_scaling,KMeansSMOTE,dart,0.1,100,0.1,0.1,1.0,1.0,10,5,0.98804128264537,0.8754763636546178,0.8405284491350876,0.8209184704711975,0.9551236805467838,0.9938696715557122,0.9965444000780066,0.9983356757019992,0.9894439864987652,0.7570830557535235,0.6845124981921686,0.6435012652403957,0.9208033745948025,104.0,5398.6,9.0,57.6
lightgbm,robust_scaling,class_weight,gbdt,0.03,100,0.1,0.1,0.8,0.8,10,5,0.9872871528817676,0.8635827226233982,0.82363889290808,0.8019139194786398,0.9607516165514827,0.9934880771322151,0.9966119285054977,0.9987055323856977,0.9883253364382266,0.7336773681145814,0.6506658573106621,0.605122306571582,0.9331778966647388,97.8,5400.6,7.0,63.8
lightgbm,robust_scaling,class_weight,gbdt,0.03,100,0.1,0.1,0.8,1.0,10,5,0.9873948857051393,0.8660654376430875,0.8278053873421346,0.8067827329934085,0.9571174874451384,0.9935416656406844,0.9965229981920896,0.9985206074636975,0.9886125885446072,0.7385892096454906,0.6590877764921795,0.6150448585231194,0.9256223863456696,99.4,5399.6,8.0,62.2
lightgbm,robust_scaling,class_weight,gbdt,0.03,100,0.1,0.1,1.0,0.8,10,5,0.9872871270913975,0.8636064344844797,0.8240482741313839,0.8025088748233425,0.9596889613355953,0.9934880088638021,0.9965897765183312,0.9986685432974788,0.9883616207181698,0.7337248601051574,0.6515067717444367,0.6063492063492064,0.9310163019530208,98.0,5400.4,7.2,63.6
lightgbm,robust_scaling,class_weight,gbdt,0.03,100,0.1,0.1,1.0,1.0,10,5,0.9871434811777045,0.863449771888091,0.8256341947711338,0.8048454076194018,0.9533828520533423,0.9934127773860718,0.9963827135574705,0.9983726647902182,0.9885024551041862,0.7334867663901103,0.6548856759847967,0.6113181504485852,0.9182632490024984,98.8,5398.8,8.8,62.8
lightgbm,robust_scaling,class_weight,gbdt,0.05,100,0.1,0.1,0.8,0.8,10,5,0.987358998405188,0.8658212328853709,0.8277045950376559,0.8067604078073429,0.9566247977036533,0.9935230811207543,0.9964934015324072,0.998483625215177,0.9886121814446716,0.7381193846499876,0.6589157885429044,0.6150371903995093,0.9246374139626351,99.4,5399.4,8.2,62.2
lightgbm,robust_scaling,class_weight,gbdt,0.05,100,0.1,0.1,0.8,1.0,10,5,0.9876462902325743,0.8702280820945317,0.8337605692544805,0.8135218826445911,0.9553624954113376,0.9936685848798849,0.9964855981899191,0.9983726511108216,0.9890093876458101,0.7467875793091783,0.6710355403190421,0.6286711141783605,0.9217156031768651,101.6,5398.8,8.8,60.0
lightgbm,robust_scaling,class_weight,gbdt,0.05,100,0.1,0.1,1.0,0.8,10,5,0.9876103706946605,0.8685306532899453,0.8303217288431941,0.8092965157481593,0.9592309417836985,0.9936518407305781,0.9966115244112512,0.9985945651210407,0.9887583083957047,0.7434094658493124,0.6640319332751371,0.619998466375278,0.9297035751716924,100.2,5400.0,7.6,61.4
lightgbm,robust_scaling,class_weight,gbdt,0.05,100,0.1,0.1,1.0,1.0,10,5,0.9875744640519315,0.8697383859570031,0.8335824387321773,0.8134887344578754,0.9538702717550708,0.993631447606049,0.9964264483135749,0.9982986866137802,0.9890083881850659,0.7458453243079577,0.6707384291507799,0.6286787823019708,0.9187321553250758,101.6,5398.4,9.2,60.0
lightgbm,robust_scaling,class_weight,gbdt,0.1,100,0.1,0.1,0.8,0.8,10,5,0.9876463031277595,0.8696452935291772,0.832345622530247,0.8117216735235644,0.9573224840422384,0.9936693371232203,0.99655223796351,0.9984836046960821,0.988901862104868,0.7456212499351342,0.6681390070969839,0.6249597423510467,0.9257431059796088,101.0,5399.4,8.2,60.6
lightgbm,robust_scaling,class_weight,gbdt,0.1,100,0.1,0.1,0.8,1.0,10,5,0.9879335756123682,0.8735142470990855,0.8373885067575266,0.81727407420208,0.9572776440521376,0.9938154842661611,0.9965889028545231,0.9984466224475614,0.9892278794141068,0.7532130099320097,0.6781881106605302,0.6361015259565985,0.9253274086901682,102.8,5399.2,8.4,58.8
lightgbm,robust_scaling,class_weight,gbdt,0.1,100,0.1,0.1,1.0,0.8,10,5,0.9874667118857822,0.8680481539924493,0.8317553825774301,0.8116330485442184,0.9529694195282257,0.993576898041232,0.9964044843956639,0.9982986866137802,0.9889004742854578,0.7425194099436665,0.6671062807591965,0.6249674104746569,0.9170383647709939,101.0,5398.4,9.2,60.6
lightgbm,robust_scaling,class_weight,gbdt,0.1,100,0.1,0.1,1.0,1.0,10,5,0.9879694887026897,0.8749776173022346,0.840353596310616,0.8209121572972682,0.9537927663883565,0.9938325369626163,0.9964852212462812,0.9982617043652595,0.9894432107974284,0.7561226976418527,0.6842219713749508,0.6435626102292769,0.9181423219792844,104.0,5398.2,9.4,57.6
lightgbm,robust_scaling,class_weight,dart,0.03,100,0.1,0.1,0.8,0.8,10,5,0.9864612162803,0.8517213882755159,0.8087404085309796,0.785885510418824,0.9614784852134713,0.9930685455041811,0.9965093140147973,0.9988164928106563,0.9873866482719492,0.7103742310468509,0.620971503047162,0.5729545280269918,0.9355703221549934,92.6,5401.2,6.4,69.0
lightgbm,robust_scaling,class_weight,dart,0.03,100,0.1,0.1,0.8,1.0,10,5,0.9862816572762855,0.8494531720877854,0.8062811698729515,0.7834055481958557,0.9607205770272069,0.9929769287184236,0.9964502905368763,0.9987795037224373,0.9872419067787573,0.7059294154571474,0.6161120492090266,0.5680315926692738,0.9341992472756562,91.8,5401.0,6.6,69.8
lightgbm,robust_scaling,class_weight,dart,0.03,100,0.1,0.1,1.0,0.8,10,5,0.9868203342883293,0.8567123113396862,0.81462313212734,0.7920698521104123,0.9624609919498163,0.993251161582376,0.9965828464697711,0.9988164928106563,0.9877478834902378,0.7201734610969963,0.632663417784909,0.585323211410168,0.9371741004093945,94.6,5401.2,6.4,67.0
lightgbm,robust_scaling,class_weight,dart,0.03,100,0.1,0.1,1.0,1.0,10,5,0.9863893900996572,0.8516614690849792,0.8095557057072785,0.7870576057488851,0.9579981706050438,0.9930309459196085,0.9964058116257544,0.9986685432974787,0.9874568744274971,0.7102919922503499,0.6227055997888027,0.5754466682002913,0.9285394667825905,93.0,5400.4,7.2,68.6
lightgbm,robust_scaling,class_weight,dart,0.05,100,0.1,0.1,0.8,0.8,10,5,0.9872871464341749,0.8647825564498515,0.8265211036628628,0.8055143377206931,0.9560208920519685,0.9934866215661462,0.9964787449584944,0.998483625215177,0.9885398629294059,0.7360784913335567,0.6565634623672312,0.6125450502262096,0.9235019211745309,99.0,5399.4,8.2,62.6
lightgbm,robust_scaling,class_weight,dart,0.05,100,0.1,0.1,0.8,1.0,10,5,0.9872512526866313,0.864242117798965,0.8255296821802522,0.8043135971909393,0.9568082528265623,0.9934684617996755,0.9964935591422892,0.9985206074636975,0.9884674795460324,0.7350157737982544,0.6545658052182155,0.6101065869181811,0.9251490261070924,98.6,5399.6,8.0,63.0
lightgbm,robust_scaling,class_weight,dart,0.05,100,0.1,0.1,1.0,0.8,10,5,0.9875385574092025,0.867739957700573,0.8292362302142348,0.808058120624818,0.9590571825576781,0.9936151348160316,0.9965968036054408,0.9985945788004372,0.9886852713014047,0.7418647805851146,0.6618756568230287,0.6175216624491988,0.9294290938139514,99.8,5400.0,7.6,61.8
lightgbm,robust_scaling,class_weight,dart,0.05,100,0.1,0.1,1.0,1.0,10,5,0.9878617429841328,0.8718918805916948,0.8343953837040834,0.8136405111931599,0.9597257588431093,0.9937798152798208,0.9966630340635951,0.998594571960739,0.9890116522948971,0.7500039459035688,0.6721277333445717,0.6286864504255809,0.9304398653913216,101.6,5400.0,7.6,60.0
lightgbm,robust_scaling,class_weight,dart,0.1,100,0.1,0.1,0.8,0.8,10,5,0.9875744511567464,0.8694904253032434,0.8330898747073772,0.8128784428659526,0.9543165483350643,0.9936317630222906,0.9964486981215537,0.9983356757019992,0.9889726061606664,0.7453490875841962,0.6697310512932007,0.6274212100299057,0.9196604905094622,101.4,5398.6,9.0,60.2
lightgbm,robust_scaling,class_weight,dart,0.1,100,0.1,0.1,0.8,1.0,10,5,0.9877540230559461,0.8705274306318904,0.8326760658152557,0.8117771571558929,0.9596652466682235,0.993724889952756,0.9966409360538494,0.998594571960739,0.9889028548898798,0.7473299713110246,0.6687111955766621,0.6249597423510467,0.930427638446567,101.0,5400.0,7.6,60.6
lightgbm,robust_scaling,class_weight,dart,0.1,100,0.1,0.1,1.0,0.8,10,5,0.9878976496268617,0.8740424916234224,0.8391944377539111,0.819654574765656,0.9536373604366759,0.9937959382660164,0.9964704965030846,0.9982616838461646,0.9893706349659366,0.7542890449808285,0.681918379004738,0.6410474656851469,0.9179040859074152,103.6,5398.2,9.4,58.0
lightgbm,robust_scaling,class_weight,dart,0.1,100,0.1,0.1,1.0,1.0,10,5,0.98804128264537,0.8754763636546178,0.8405284491350876,0.8209184704711975,0.9551236805467838,0.9938696715557122,0.9965444000780066,0.9983356757019992,0.9894439864987652,0.7570830557535235,0.6845124981921686,0.6435012652403957,0.9208033745948025,104.0,5398.6,9.0,57.6
lightgbm,minmax_scaling,KMeansSMOTE,gbdt,0.03,100,0.1,0.1,0.8,0.8,10,5,0.9872871528817676,0.8635827226233982,0.82363889290808,0.8019139194786398,0.9607516165514827,0.9934880771322151,0.9966119285054977,0.9987055323856977,0.9883253364382266,0.7336773681145814,0.6506658573106621,0.605122306571582,0.9331778966647388,97.8,5400.6,7.0,63.8
lightgbm,minmax_scaling,KMeansSMOTE,gbdt,0.03,100,0.1,0.1,0.8,1.0,10,5,0.9873948857051393,0.8660654376430875,0.8278053873421346,0.8067827329934085,0.9571174874451384,0.9935416656406844,0.9965229981920896,0.9985206074636975,0.9886125885446072,0.7385892096454906,0.6590877764921795,0.6150448585231194,0.9256223863456696,99.4,5399.6,8.0,62.2
lightgbm,minmax_scaling,KMeansSMOTE,gbdt,0.03,100,0.1,0.1,1.0,0.8,10,5,0.9872871270913975,0.8636064344844797,0.8240482741313839,0.8025088748233425,0.9596889613355953,0.9934880088638021,0.9965897765183312,0.9986685432974788,0.9883616207181698,0.7337248601051574,0.6515067717444367,0.6063492063492064,0.9310163019530208,98.0,5400.4,7.2,63.6
lightgbm,minmax_scaling,KMeansSMOTE,gbdt,0.03,100,0.1,0.1,1.0,1.0,10,5,0.9871434811777045,0.863449771888091,0.8256341947711338,0.8048454076194018,0.9533828520533423,0.9934127773860718,0.9963827135574705,0.9983726647902182,0.9885024551041862,0.7334867663901103,0.6548856759847967,0.6113181504485852,0.9182632490024984,98.8,5398.8,8.8,62.8
lightgbm,minmax_scaling,KMeansSMOTE,gbdt,0.05,100,0.1,0.1,0.8,0.8,10,5,0.987358998405188,0.8658212328853709,0.8277045950376559,0.8067604078073429,0.9566247977036533,0.9935230811207543,0.9964934015324072,0.998483625215177,0.9886121814446716,0.7381193846499876,0.6589157885429044,0.6150371903995093,0.9246374139626351,99.4,5399.4,8.2,62.2
lightgbm,minmax_scaling,KMeansSMOTE,gbdt,0.05,100,0.1,0.1,0.8,1.0,10,5,0.9876462902325743,0.8702280820945317,0.8337605692544805,0.8135218826445911,0.9553624954113376,0.9936685848798849,0.9964855981899191,0.9983726511108216,0.9890093876458101,0.7467875793091783,0.6710355403190421,0.6286711141783605,0.9217156031768651,101.6,5398.8,8.8,60.0
lightgbm,minmax_scaling,KMeansSMOTE,gbdt,0.05,100,0.1,0.1,1.0,0.8,10,5,0.9876103706946605,0.8685306532899453,0.8303217288431941,0.8092965157481593,0.9592309417836985,0.9936518407305781,0.9966115244112512,0.9985945651210407,0.9887583083957047,0.7434094658493124,0.6640319332751371,0.619998466375278,0.9297035751716924,100.2,5400.0,7.6,61.4
lightgbm,minmax_scaling,KMeansSMOTE,gbdt,0.05,100,0.1,0.1,1.0,1.0,10,5,0.9875744640519315,0.8697383859570031,0.8335824387321773,0.8134887344578754,0.9538702717550708,0.993631447606049,0.9964264483135749,0.9982986866137802,0.9890083881850659,0.7458453243079577,0.6707384291507799,0.6286787823019708,0.9187321553250758,101.6,5398.4,9.2,60.0
lightgbm,minmax_scaling,KMeansSMOTE,gbdt,0.1,100,0.1,0.1,0.8,0.8,10,5,0.9876463031277595,0.8696452935291772,0.832345622530247,0.8117216735235644,0.9573224840422384,0.9936693371232203,0.99655223796351,0.9984836046960821,0.988901862104868,0.7456212499351342,0.6681390070969839,0.6249597423510467,0.9257431059796088,101.0,5399.4,8.2,60.6
lightgbm,minmax_scaling,KMeansSMOTE,gbdt,0.1,100,0.1,0.1,0.8,1.0,10,5,0.9879335756123682,0.8735142470990855,0.8373885067575266,0.81727407420208,0.9572776440521376,0.9938154842661611,0.9965889028545231,0.9984466224475614,0.9892278794141068,0.7532130099320097,0.6781881106605302,0.6361015259565985,0.9253274086901682,102.8,5399.2,8.4,58.8
lightgbm,minmax_scaling,KMeansSMOTE,gbdt,0.1,100,0.1,0.1,1.0,0.8,10,5,0.9874667118857822,0.8680481539924493,0.8317553825774301,0.8116330485442184,0.9529694195282257,0.993576898041232,0.9964044843956639,0.9982986866137802,0.9889004742854578,0.7425194099436665,0.6671062807591965,0.6249674104746569,0.9170383647709939,101.0,5398.4,9.2,60.6
lightgbm,minmax_scaling,KMeansSMOTE,gbdt,0.1,100,0.1,0.1,1.0,1.0,10,5,0.9879694887026897,0.8749776173022346,0.840353596310616,0.8209121572972682,0.9537927663883565,0.9938325369626163,0.9964852212462812,0.9982617043652595,0.9894432107974284,0.7561226976418527,0.6842219713749508,0.6435626102292769,0.9181423219792844,104.0,5398.2,9.4,57.6
lightgbm,minmax_scaling,KMeansSMOTE,dart,0.03,100,0.1,0.1,0.8,0.8,10,5,0.9864612162803,0.8517213882755159,0.8087404085309796,0.785885510418824,0.9614784852134713,0.9930685455041811,0.9965093140147973,0.9988164928106563,0.9873866482719492,0.7103742310468509,0.620971503047162,0.5729545280269918,0.9355703221549934,92.6,5401.2,6.4,69.0
lightgbm,minmax_scaling,KMeansSMOTE,dart,0.03,100,0.1,0.1,0.8,1.0,10,5,0.9862816572762855,0.8494531720877854,0.8062811698729515,0.7834055481958557,0.9607205770272069,0.9929769287184236,0.9964502905368763,0.9987795037224373,0.9872419067787573,0.7059294154571474,0.6161120492090266,0.5680315926692738,0.9341992472756562,91.8,5401.0,6.6,69.8
lightgbm,minmax_scaling,KMeansSMOTE,dart,0.03,100,0.1,0.1,1.0,0.8,10,5,0.9868203342883293,0.8567123113396862,0.81462313212734,0.7920698521104123,0.9624609919498163,0.993251161582376,0.9965828464697711,0.9988164928106563,0.9877478834902378,0.7201734610969963,0.632663417784909,0.585323211410168,0.9371741004093945,94.6,5401.2,6.4,67.0
lightgbm,minmax_scaling,KMeansSMOTE,dart,0.03,100,0.1,0.1,1.0,1.0,10,5,0.9863893900996572,0.8516614690849792,0.8095557057072785,0.7870576057488851,0.9579981706050438,0.9930309459196085,0.9964058116257544,0.9986685432974787,0.9874568744274971,0.7102919922503499,0.6227055997888027,0.5754466682002913,0.9285394667825905,93.0,5400.4,7.2,68.6
lightgbm,minmax_scaling,KMeansSMOTE,dart,0.05,100,0.1,0.1,0.8,0.8,10,5,0.9872871464341749,0.8647825564498515,0.8265211036628628,0.8055143377206931,0.9560208920519685,0.9934866215661462,0.9964787449584944,0.998483625215177,0.9885398629294059,0.7360784913335567,0.6565634623672312,0.6125450502262096,0.9235019211745309,99.0,5399.4,8.2,62.6
lightgbm,minmax_scaling,KMeansSMOTE,dart,0.05,100,0.1,0.1,0.8,1.0,10,5,0.9872512526866313,0.864242117798965,0.8255296821802522,0.8043135971909393,0.9568082528265623,0.9934684617996755,0.9964935591422892,0.9985206074636975,0.9884674795460324,0.7350157737982544,0.6545658052182155,0.6101065869181811,0.9251490261070924,98.6,5399.6,8.0,63.0
lightgbm,minmax_scaling,KMeansSMOTE,dart,0.05,100,0.1,0.1,1.0,0.8,10,5,0.9875385574092025,0.867739957700573,0.8292362302142348,0.808058120624818,0.9590571825576781,0.9936151348160316,0.9965968036054408,0.9985945788004372,0.9886852713014047,0.7418647805851146,0.6618756568230287,0.6175216624491988,0.9294290938139514,99.8,5400.0,7.6,61.8
lightgbm,minmax_scaling,KMeansSMOTE,dart,0.05,100,0.1,0.1,1.0,1.0,10,5,0.9878617429841328,0.8718918805916948,0.8343953837040834,0.8136405111931599,0.9597257588431093,0.9937798152798208,0.9966630340635951,0.998594571960739,0.9890116522948971,0.7500039459035688,0.6721277333445717,0.6286864504255809,0.9304398653913216,101.6,5400.0,7.6,60.0
lightgbm,minmax_scaling,KMeansSMOTE,dart,0.1,100,0.1,0.1,0.8,0.8,10,5,0.9875744511567464,0.8694904253032434,0.8330898747073772,0.8128784428659526,0.9543165483350643,0.9936317630222906,0.9964486981215537,0.9983356757019992,0.9889726061606664,0.7453490875841962,0.6697310512932007,0.6274212100299057,0.9196604905094622,101.4,5398.6,9.0,60.2
lightgbm,minmax_scaling,KMeansSMOTE,dart,0.1,100,0.1,0.1,0.8,1.0,10,5,0.9877540230559461,0.8705274306318904,0.8326760658152557,0.8117771571558929,0.9596652466682235,0.993724889952756,0.9966409360538494,0.998594571960739,0.9889028548898798,0.7473299713110246,0.6687111955766621,0.6249597423510467,0.930427638446567,101.0,5400.0,7.6,60.6
lightgbm,minmax_scaling,KMeansSMOTE,dart,0.1,100,0.1,0.1,1.0,0.8,10,5,0.9878976496268617,0.8740424916234224,0.8391944377539111,0.819654574765656,0.9536373604366759,0.9937959382660164,0.9964704965030846,0.9982616838461646,0.9893706349659366,0.7542890449808285,0.681918379004738,0.6410474656851469,0.9179040859074152,103.6,5398.2,9.4,58.0
lightgbm,minmax_scaling,KMeansSMOTE,dart,0.1,100,0.1,0.1,1.0,1.0,10,5,0.98804128264537,0.8754763636546178,0.8405284491350876,0.8209184704711975,0.9551236805467838,0.9938696715557122,0.9965444000780066,0.9983356757019992,0.9894439864987652,0.7570830557535235,0.6845124981921686,0.6435012652403957,0.9208033745948025,104.0,5398.6,9.0,57.6
lightgbm,minmax_scaling,class_weight,gbdt,0.03,100,0.1,0.1,0.8,0.8,10,5,0.9872871528817676,0.8635827226233982,0.82363889290808,0.8019139194786398,0.9607516165514827,0.9934880771322151,0.9966119285054977,0.9987055323856977,0.9883253364382266,0.7336773681145814,0.6506658573106621,0.605122306571582,0.9331778966647388,97.8,5400.6,7.0,63.8
lightgbm,minmax_scaling,class_weight,gbdt,0.03,100,0.1,0.1,0.8,1.0,10,5,0.9873948857051393,0.8660654376430875,0.8278053873421346,0.8067827329934085,0.9571174874451384,0.9935416656406844,0.9965229981920896,0.9985206074636975,0.9886125885446072,0.7385892096454906,0.6590877764921795,0.6150448585231194,0.9256223863456696,99.4,5399.6,8.0,62.2
lightgbm,minmax_scaling,class_weight,gbdt,0.03,100,0.1,0.1,1.0,0.8,10,5,0.9872871270913975,0.8636064344844797,0.8240482741313839,0.8025088748233425,0.9596889613355953,0.9934880088638021,0.9965897765183312,0.9986685432974788,0.9883616207181698,0.7337248601051574,0.6515067717444367,0.6063492063492064,0.9310163019530208,98.0,5400.4,7.2,63.6
lightgbm,minmax_scaling,class_weight,gbdt,0.03,100,0.1,0.1,1.0,1.0,10,5,0.9871434811777045,0.863449771888091,0.8256341947711338,0.8048454076194018,0.9533828520533423,0.9934127773860718,0.9963827135574705,0.9983726647902182,0.9885024551041862,0.7334867663901103,0.6548856759847967,0.6113181504485852,0.9182632490024984,98.8,5398.8,8.8,62.8
lightgbm,minmax_scaling,class_weight,gbdt,0.05,100,0.1,0.1,0.8,0.8,10,5,0.987358998405188,0.8658212328853709,0.8277045950376559,0.8067604078073429,0.9566247977036533,0.9935230811207543,0.9964934015324072,0.998483625215177,0.9886121814446716,0.7381193846499876,0.6589157885429044,0.6150371903995093,0.9246374139626351,99.4,5399.4,8.2,62.2
lightgbm,minmax_scaling,class_weight,gbdt,0.05,100,0.1,0.1,0.8,1.0,10,5,0.9876462902325743,0.8702280820945317,0.8337605692544805,0.8135218826445911,0.9553624954113376,0.9936685848798849,0.9964855981899191,0.9983726511108216,0.9890093876458101,0.7467875793091783,0.6710355403190421,0.6286711141783605,0.9217156031768651,101.6,5398.8,8.8,60.0
lightgbm,minmax_scaling,class_weight,gbdt,0.05,100,0.1,0.1,1.0,0.8,10,5,0.9876103706946605,0.8685306532899453,0.8303217288431941,0.8092965157481593,0.9592309417836985,0.9936518407305781,0.9966115244112512,0.9985945651210407,0.9887583083957047,0.7434094658493124,0.6640319332751371,0.619998466375278,0.9297035751716924,100.2,5400.0,7.6,61.4
lightgbm,minmax_scaling,class_weight,gbdt,0.05,100,0.1,0.1,1.0,1.0,10,5,0.9875744640519315,0.8697383859570031,0.8335824387321773,0.8134887344578754,0.9538702717550708,0.993631447606049,0.9964264483135749,0.9982986866137802,0.9890083881850659,0.7458453243079577,0.6707384291507799,0.6286787823019708,0.9187321553250758,101.6,5398.4,9.2,60.0
lightgbm,minmax_scaling,class_weight,gbdt,0.1,100,0.1,0.1,0.8,0.8,10,5,0.9876463031277595,0.8696452935291772,0.832345622530247,0.8117216735235644,0.9573224840422384,0.9936693371232203,0.99655223796351,0.9984836046960821,0.988901862104868,0.7456212499351342,0.6681390070969839,0.6249597423510467,0.9257431059796088,101.0,5399.4,8.2,60.6
lightgbm,minmax_scaling,class_weight,gbdt,0.1,100,0.1,0.1,0.8,1.0,10,5,0.9879335756123682,0.8735142470990855,0.8373885067575266,0.81727407420208,0.9572776440521376,0.9938154842661611,0.9965889028545231,0.9984466224475614,0.9892278794141068,0.7532130099320097,0.6781881106605302,0.6361015259565985,0.9253274086901682,102.8,5399.2,8.4,58.8
lightgbm,minmax_scaling,class_weight,gbdt,0.1,100,0.1,0.1,1.0,0.8,10,5,0.9874667118857822,0.8680481539924493,0.8317553825774301,0.8116330485442184,0.9529694195282257,0.993576898041232,0.9964044843956639,0.9982986866137802,0.9889004742854578,0.7425194099436665,0.6671062807591965,0.6249674104746569,0.9170383647709939,101.0,5398.4,9.2,60.6
lightgbm,minmax_scaling,class_weight,gbdt,0.1,100,0.1,0.1,1.0,1.0,10,5,0.9879694887026897,0.8749776173022346,0.840353596310616,0.8209121572972682,0.9537927663883565,0.9938325369626163,0.9964852212462812,0.9982617043652595,0.9894432107974284,0.7561226976418527,0.6842219713749508,0.6435626102292769,0.9181423219792844,104.0,5398.2,9.4,57.6
lightgbm,minmax_scaling,class_weight,dart,0.03,100,0.1,0.1,0.8,0.8,10,5,0.9864612162803,0.8517213882755159,0.8087404085309796,0.785885510418824,0.9614784852134713,0.9930685455041811,0.9965093140147973,0.9988164928106563,0.9873866482719492,0.7103742310468509,0.620971503047162,0.5729545280269918,0.9355703221549934,92.6,5401.2,6.4,69.0
lightgbm,minmax_scaling,class_weight,dart,0.03,100,0.1,0.1,0.8,1.0,10,5,0.9862816572762855,0.8494531720877854,0.8062811698729515,0.7834055481958557,0.9607205770272069,0.9929769287184236,0.9964502905368763,0.9987795037224373,0.9872419067787573,0.7059294154571474,0.6161120492090266,0.5680315926692738,0.9341992472756562,91.8,5401.0,6.6,69.8
lightgbm,minmax_scaling,class_weight,dart,0.03,100,0.1,0.1,1.0,0.8,10,5,0.9868203342883293,0.8567123113396862,0.81462313212734,0.7920698521104123,0.9624609919498163,0.993251161582376,0.9965828464697711,0.9988164928106563,0.9877478834902378,0.7201734610969963,0.632663417784909,0.585323211410168,0.9371741004093945,94.6,5401.2,6.4,67.0
lightgbm,minmax_scaling,class_weight,dart,0.03,100,0.1,0.1,1.0,1.0,10,5,0.9863893900996572,0.8516614690849792,0.8095557057072785,0.7870576057488851,0.9579981706050438,0.9930309459196085,0.9964058116257544,0.9986685432974787,0.9874568744274971,0.7102919922503499,0.6227055997888027,0.5754466682002913,0.9285394667825905,93.0,5400.4,7.2,68.6
lightgbm,minmax_scaling,class_weight,dart,0.05,100,0.1,0.1,0.8,0.8,10,5,0.9872871464341749,0.8647825564498515,0.8265211036628628,0.8055143377206931,0.9560208920519685,0.9934866215661462,0.9964787449584944,0.998483625215177,0.9885398629294059,0.7360784913335567,0.6565634623672312,0.6125450502262096,0.9235019211745309,99.0,5399.4,8.2,62.6
lightgbm,minmax_scaling,class_weight,dart,0.05,100,0.1,0.1,0.8,1.0,10,5,0.9872512526866313,0.864242117798965,0.8255296821802522,0.8043135971909393,0.9568082528265623,0.9934684617996755,0.9964935591422892,0.9985206074636975,0.9884674795460324,0.7350157737982544,0.6545658052182155,0.6101065869181811,0.9251490261070924,98.6,5399.6,8.0,63.0
lightgbm,minmax_scaling,class_weight,dart,0.05,100,0.1,0.1,1.0,0.8,10,5,0.9875385574092025,0.867739957700573,0.8292362302142348,0.808058120624818,0.9590571825576781,0.9936151348160316,0.9965968036054408,0.9985945788004372,0.9886852713014047,0.7418647805851146,0.6618756568230287,0.6175216624491988,0.9294290938139514,99.8,5400.0,7.6,61.8
lightgbm,minmax_scaling,class_weight,dart,0.05,100,0.1,0.1,1.0,1.0,10,5,0.9878617429841328,0.8718918805916948,0.8343953837040834,0.8136405111931599,0.9597257588431093,0.9937798152798208,0.9966630340635951,0.998594571960739,0.9890116522948971,0.7500039459035688,0.6721277333445717,0.6286864504255809,0.9304398653913216,101.6,5400.0,7.6,60.0
lightgbm,minmax_scaling,class_weight,dart,0.1,100,0.1,0.1,0.8,0.8,10,5,0.9875744511567464,0.8694904253032434,0.8330898747073772,0.8128784428659526,0.9543165483350643,0.9936317630222906,0.9964486981215537,0.9983356757019992,0.9889726061606664,0.7453490875841962,0.6697310512932007,0.6274212100299057,0.9196604905094622,101.4,5398.6,9.0,60.2
lightgbm,minmax_scaling,class_weight,dart,0.1,100,0.1,0.1,0.8,1.0,10,5,0.9877540230559461,0.8705274306318904,0.8326760658152557,0.8117771571558929,0.9596652466682235,0.993724889952756,0.9966409360538494,0.998594571960739,0.9889028548898798,0.7473299713110246,0.6687111955766621,0.6249597423510467,0.930427638446567,101.0,5400.0,7.6,60.6
lightgbm,minmax_scaling,class_weight,dart,0.1,100,0.1,0.1,1.0,0.8,10,5,0.9878976496268617,0.8740424916234224,0.8391944377539111,0.819654574765656,0.9536373604366759,0.9937959382660164,0.9964704965030846,0.9982616838461646,0.9893706349659366,0.7542890449808285,0.681918379004738,0.6410474656851469,0.9179040859074152,103.6,5398.2,9.4,58.0
lightgbm,minmax_scaling,class_weight,dart,0.1,100,0.1,0.1,1.0,1.0,10,5,0.98804128264537,0.8754763636546178,0.8405284491350876,0.8209184704711975,0.9551236805467838,0.9938696715557122,0.9965444000780066,0.9983356757019992,0.9894439864987652,0.7570830557535235,0.6845124981921686,0.6435012652403957,0.9208033745948025,104.0,5398.6,9.0,57.6
lightgbm,yeo_johnson,KMeansSMOTE,gbdt,0.03,100,0.1,0.1,0.8,0.8,10,5,0.9872871528817676,0.8635827226233982,0.82363889290808,0.8019139194786398,0.9607516165514827,0.9934880771322151,0.9966119285054977,0.9987055323856977,0.9883253364382266,0.7336773681145814,0.6506658573106621,0.605122306571582,0.9331778966647388,97.8,5400.6,7.0,63.8
lightgbm,yeo_johnson,KMeansSMOTE,gbdt,0.03,100,0.1,0.1,0.8,1.0,10,5,0.9873948857051393,0.8660654376430875,0.8278053873421346,0.8067827329934085,0.9571174874451384,0.9935416656406844,0.9965229981920896,0.9985206074636975,0.9886125885446072,0.7385892096454906,0.6590877764921795,0.6150448585231194,0.9256223863456696,99.4,5399.6,8.0,62.2
lightgbm,yeo_johnson,KMeansSMOTE,gbdt,0.03,100,0.1,0.1,1.0,0.8,10,5,0.9872871270913975,0.8636064344844797,0.8240482741313839,0.8025088748233425,0.9596889613355953,0.9934880088638021,0.9965897765183312,0.9986685432974788,0.9883616207181698,0.7337248601051574,0.6515067717444367,0.6063492063492064,0.9310163019530208,98.0,5400.4,7.2,63.6
lightgbm,yeo_johnson,KMeansSMOTE,gbdt,0.03,100,0.1,0.1,1.0,1.0,10,5,0.9871434811777045,0.863449771888091,0.8256341947711338,0.8048454076194018,0.9533828520533423,0.9934127773860718,0.9963827135574705,0.9983726647902182,0.9885024551041862,0.7334867663901103,0.6548856759847967,0.6113181504485852,0.9182632490024984,98.8,5398.8,8.8,62.8
lightgbm,yeo_johnson,KMeansSMOTE,gbdt,0.05,100,0.1,0.1,0.8,0.8,10,5,0.987358998405188,0.8658212328853709,0.8277045950376559,0.8067604078073429,0.9566247977036533,0.9935230811207543,0.9964934015324072,0.998483625215177,0.9886121814446716,0.7381193846499876,0.6589157885429044,0.6150371903995093,0.9246374139626351,99.4,5399.4,8.2,62.2
lightgbm,yeo_johnson,KMeansSMOTE,gbdt,0.05,100,0.1,0.1,0.8,1.0,10,5,0.9876462902325743,0.8702280820945317,0.8337605692544805,0.8135218826445911,0.9553624954113376,0.9936685848798849,0.9964855981899191,0.9983726511108216,0.9890093876458101,0.7467875793091783,0.6710355403190421,0.6286711141783605,0.9217156031768651,101.6,5398.8,8.8,60.0
lightgbm,yeo_johnson,KMeansSMOTE,gbdt,0.05,100,0.1,0.1,1.0,0.8,10,5,0.9876103706946605,0.8685306532899453,0.8303217288431941,0.8092965157481593,0.9592309417836985,0.9936518407305781,0.9966115244112512,0.9985945651210407,0.9887583083957047,0.7434094658493124,0.6640319332751371,0.619998466375278,0.9297035751716924,100.2,5400.0,7.6,61.4
lightgbm,yeo_johnson,KMeansSMOTE,gbdt,0.05,100,0.1,0.1,1.0,1.0,10,5,0.9875744640519315,0.8697383859570031,0.8335824387321773,0.8134887344578754,0.9538702717550708,0.993631447606049,0.9964264483135749,0.9982986866137802,0.9890083881850659,0.7458453243079577,0.6707384291507799,0.6286787823019708,0.9187321553250758,101.6,5398.4,9.2,60.0
lightgbm,yeo_johnson,KMeansSMOTE,gbdt,0.1,100,0.1,0.1,0.8,0.8,10,5,0.9876463031277595,0.8696452935291772,0.832345622530247,0.8117216735235644,0.9573224840422384,0.9936693371232203,0.99655223796351,0.9984836046960821,0.988901862104868,0.7456212499351342,0.6681390070969839,0.6249597423510467,0.9257431059796088,101.0,5399.4,8.2,60.6
lightgbm,yeo_johnson,KMeansSMOTE,gbdt,0.1,100,0.1,0.1,0.8,1.0,10,5,0.9879335756123682,0.8735142470990855,0.8373885067575266,0.81727407420208,0.9572776440521376,0.9938154842661611,0.9965889028545231,0.9984466224475614,0.9892278794141068,0.7532130099320097,0.6781881106605302,0.6361015259565985,0.9253274086901682,102.8,5399.2,8.4,58.8
lightgbm,yeo_johnson,KMeansSMOTE,gbdt,0.1,100,0.1,0.1,1.0,0.8,10,5,0.9874667118857822,0.8680481539924493,0.8317553825774301,0.8116330485442184,0.9529694195282257,0.993576898041232,0.9964044843956639,0.9982986866137802,0.9889004742854578,0.7425194099436665,0.6671062807591965,0.6249674104746569,0.9170383647709939,101.0,5398.4,9.2,60.6
lightgbm,yeo_johnson,KMeansSMOTE,gbdt,0.1,100,0.1,0.1,1.0,1.0,10,5,0.9879694887026897,0.8749776173022346,0.840353596310616,0.8209121572972682,0.9537927663883565,0.9938325369626163,0.9964852212462812,0.9982617043652595,0.9894432107974284,0.7561226976418527,0.6842219713749508,0.6435626102292769,0.9181423219792844,104.0,5398.2,9.4,57.6
lightgbm,yeo_johnson,KMeansSMOTE,dart,0.03,100,0.1,0.1,0.8,0.8,10,5,0.9864612162803,0.8517213882755159,0.8087404085309796,0.785885510418824,0.9614784852134713,0.9930685455041811,0.9965093140147973,0.9988164928106563,0.9873866482719492,0.7103742310468509,0.620971503047162,0.5729545280269918,0.9355703221549934,92.6,5401.2,6.4,69.0
lightgbm,yeo_johnson,KMeansSMOTE,dart,0.03,100,0.1,0.1,0.8,1.0,10,5,0.9862816572762855,0.8494531720877854,0.8062811698729515,0.7834055481958557,0.9607205770272069,0.9929769287184236,0.9964502905368763,0.9987795037224373,0.9872419067787573,0.7059294154571474,0.6161120492090266,0.5680315926692738,0.9341992472756562,91.8,5401.0,6.6,69.8
lightgbm,yeo_johnson,KMeansSMOTE,dart,0.03,100,0.1,0.1,1.0,0.8,10,5,0.9868203342883293,0.8567123113396862,0.81462313212734,0.7920698521104123,0.9624609919498163,0.993251161582376,0.9965828464697711,0.9988164928106563,0.9877478834902378,0.7201734610969963,0.632663417784909,0.585323211410168,0.9371741004093945,94.6,5401.2,6.4,67.0
lightgbm,yeo_johnson,KMeansSMOTE,dart,0.03,100,0.1,0.1,1.0,1.0,10,5,0.9863893900996572,0.8516614690849792,0.8095557057072785,0.7870576057488851,0.9579981706050438,0.9930309459196085,0.9964058116257544,0.9986685432974787,0.9874568744274971,0.7102919922503499,0.6227055997888027,0.5754466682002913,0.9285394667825905,93.0,5400.4,7.2,68.6
lightgbm,yeo_johnson,KMeansSMOTE,dart,0.05,100,0.1,0.1,0.8,0.8,10,5,0.9872871464341749,0.8647825564498515,0.8265211036628628,0.8055143377206931,0.9560208920519685,0.9934866215661462,0.9964787449584944,0.998483625215177,0.9885398629294059,0.7360784913335567,0.6565634623672312,0.6125450502262096,0.9235019211745309,99.0,5399.4,8.2,62.6
lightgbm,yeo_johnson,KMeansSMOTE,dart,0.05,100,0.1,0.1,0.8,1.0,10,5,0.9872512526866313,0.864242117798965,0.8255296821802522,0.8043135971909393,0.9568082528265623,0.9934684617996755,0.9964935591422892,0.9985206074636975,0.9884674795460324,0.7350157737982544,0.6545658052182155,0.6101065869181811,0.9251490261070924,98.6,5399.6,8.0,63.0
lightgbm,yeo_johnson,KMeansSMOTE,dart,0.05,100,0.1,0.1,1.0,0.8,10,5,0.9875385574092025,0.867739957700573,0.8292362302142348,0.808058120624818,0.9590571825576781,0.9936151348160316,0.9965968036054408,0.9985945788004372,0.9886852713014047,0.7418647805851146,0.6618756568230287,0.6175216624491988,0.9294290938139514,99.8,5400.0,7.6,61.8
lightgbm,yeo_johnson,KMeansSMOTE,dart,0.05,100,0.1,0.1,1.0,1.0,10,5,0.9878617429841328,0.8718918805916948,0.8343953837040834,0.8136405111931599,0.9597257588431093,0.9937798152798208,0.9966630340635951,0.998594571960739,0.9890116522948971,0.7500039459035688,0.6721277333445717,0.6286864504255809,0.9304398653913216,101.6,5400.0,7.6,60.0
lightgbm,yeo_johnson,KMeansSMOTE,dart,0.1,100,0.1,0.1,0.8,0.8,10,5,0.9875744511567464,0.8694904253032434,0.8330898747073772,0.8128784428659526,0.9543165483350643,0.9936317630222906,0.9964486981215537,0.9983356757019992,0.9889726061606664,0.7453490875841962,0.6697310512932007,0.6274212100299057,0.9196604905094622,101.4,5398.6,9.0,60.2
lightgbm,yeo_johnson,KMeansSMOTE,dart,0.1,100,0.1,0.1,0.8,1.0,10,5,0.9877540230559461,0.8705274306318904,0.8326760658152557,0.8117771571558929,0.9596652466682235,0.993724889952756,0.9966409360538494,0.998594571960739,0.9889028548898798,0.7473299713110246,0.6687111955766621,0.6249597423510467,0.930427638446567,101.0,5400.0,7.6,60.6
lightgbm,yeo_johnson,KMeansSMOTE,dart,0.1,100,0.1,0.1,1.0,0.8,10,5,0.9878976496268617,0.8740424916234224,0.8391944377539111,0.819654574765656,0.9536373604366759,0.9937959382660164,0.9964704965030846,0.9982616838461646,0.9893706349659366,0.7542890449808285,0.681918379004738,0.6410474656851469,0.9179040859074152,103.6,5398.2,9.4,58.0
lightgbm,yeo_johnson,KMeansSMOTE,dart,0.1,100,0.1,0.1,1.0,1.0,10,5,0.98804128264537,0.8754763636546178,0.8405284491350876,0.8209184704711975,0.9551236805467838,0.9938696715557122,0.9965444000780066,0.9983356757019992,0.9894439864987652,0.7570830557535235,0.6845124981921686,0.6435012652403957,0.9208033745948025,104.0,5398.6,9.0,57.6
lightgbm,yeo_johnson,class_weight,gbdt,0.03,100,0.1,0.1,0.8,0.8,10,5,0.9872871528817676,0.8635827226233982,0.82363889290808,0.8019139194786398,0.9607516165514827,0.9934880771322151,0.9966119285054977,0.9987055323856977,0.9883253364382266,0.7336773681145814,0.6506658573106621,0.605122306571582,0.9331778966647388,97.8,5400.6,7.0,63.8
lightgbm,yeo_johnson,class_weight,gbdt,0.03,100,0.1,0.1,0.8,1.0,10,5,0.9873948857051393,0.8660654376430875,0.8278053873421346,0.8067827329934085,0.9571174874451384,0.9935416656406844,0.9965229981920896,0.9985206074636975,0.9886125885446072,0.7385892096454906,0.6590877764921795,0.6150448585231194,0.9256223863456696,99.4,5399.6,8.0,62.2
lightgbm,yeo_johnson,class_weight,gbdt,0.03,100,0.1,0.1,1.0,0.8,10,5,0.9872871270913975,0.8636064344844797,0.8240482741313839,0.8025088748233425,0.9596889613355953,0.9934880088638021,0.9965897765183312,0.9986685432974788,0.9883616207181698,0.7337248601051574,0.6515067717444367,0.6063492063492064,0.9310163019530208,98.0,5400.4,7.2,63.6
lightgbm,yeo_johnson,class_weight,gbdt,0.03,100,0.1,0.1,1.0,1.0,10,5,0.9871434811777045,0.863449771888091,0.8256341947711338,0.8048454076194018,0.9533828520533423,0.9934127773860718,0.9963827135574705,0.9983726647902182,0.9885024551041862,0.7334867663901103,0.6548856759847967,0.6113181504485852,0.9182632490024984,98.8,5398.8,8.8,62.8
lightgbm,yeo_johnson,class_weight,gbdt,0.05,100,0.1,0.1,0.8,0.8,10,5,0.987358998405188,0.8658212328853709,0.8277045950376559,0.8067604078073429,0.9566247977036533,0.9935230811207543,0.9964934015324072,0.998483625215177,0.9886121814446716,0.7381193846499876,0.6589157885429044,0.6150371903995093,0.9246374139626351,99.4,5399.4,8.2,62.2
lightgbm,yeo_johnson,class_weight,gbdt,0.05,100,0.1,0.1,0.8,1.0,10,5,0.9876462902325743,0.8702280820945317,0.8337605692544805,0.8135218826445911,0.9553624954113376,0.9936685848798849,0.9964855981899191,0.9983726511108216,0.9890093876458101,0.7467875793091783,0.6710355403190421,0.6286711141783605,0.9217156031768651,101.6,5398.8,8.8,60.0
lightgbm,yeo_johnson,class_weight,gbdt,0.05,100,0.1,0.1,1.0,0.8,10,5,0.9876103706946605,0.8685306532899453,0.8303217288431941,0.8092965157481593,0.9592309417836985,0.9936518407305781,0.9966115244112512,0.9985945651210407,0.9887583083957047,0.7434094658493124,0.6640319332751371,0.619998466375278,0.9297035751716924,100.2,5400.0,7.6,61.4
lightgbm,yeo_johnson,class_weight,gbdt,0.05,100,0.1,0.1,1.0,1.0,10,5,0.9875744640519315,0.8697383859570031,0.8335824387321773,0.8134887344578754,0.9538702717550708,0.993631447606049,0.9964264483135749,0.9982986866137802,0.9890083881850659,0.7458453243079577,0.6707384291507799,0.6286787823019708,0.9187321553250758,101.6,5398.4,9.2,60.0
lightgbm,yeo_johnson,class_weight,gbdt,0.1,100,0.1,0.1,0.8,0.8,10,5,0.9876463031277595,0.8696452935291772,0.832345622530247,0.8117216735235644,0.9573224840422384,0.9936693371232203,0.99655223796351,0.9984836046960821,0.988901862104868,0.7456212499351342,0.6681390070969839,0.6249597423510467,0.9257431059796088,101.0,5399.4,8.2,60.6
lightgbm,yeo_johnson,class_weight,gbdt,0.1,100,0.1,0.1,0.8,1.0,10,5,0.9879335756123682,0.8735142470990855,0.8373885067575266,0.81727407420208,0.9572776440521376,0.9938154842661611,0.9965889028545231,0.9984466224475614,0.9892278794141068,0.7532130099320097,0.6781881106605302,0.6361015259565985,0.9253274086901682,102.8,5399.2,8.4,58.8
lightgbm,yeo_johnson,class_weight,gbdt,0.1,100,0.1,0.1,1.0,0.8,10,5,0.9874667118857822,0.8680481539924493,0.8317553825774301,0.8116330485442184,0.9529694195282257,0.993576898041232,0.9964044843956639,0.9982986866137802,0.9889004742854578,0.7425194099436665,0.6671062807591965,0.6249674104746569,0.9170383647709939,101.0,5398.4,9.2,60.6
lightgbm,yeo_johnson,class_weight,gbdt,0.1,100,0.1,0.1,1.0,1.0,10,5,0.9879694887026897,0.8749776173022346,0.840353596310616,0.8209121572972682,0.9537927663883565,0.9938325369626163,0.9964852212462812,0.9982617043652595,0.9894432107974284,0.7561226976418527,0.6842219713749508,0.6435626102292769,0.9181423219792844,104.0,5398.2,9.4,57.6
lightgbm,yeo_johnson,class_weight,dart,0.03,100,0.1,0.1,0.8,0.8,10,5,0.9864612162803,0.8517213882755159,0.8087404085309796,0.785885510418824,0.9614784852134713,0.9930685455041811,0.9965093140147973,0.9988164928106563,0.9873866482719492,0.7103742310468509,0.620971503047162,0.5729545280269918,0.9355703221549934,92.6,5401.2,6.4,69.0
lightgbm,yeo_johnson,class_weight,dart,0.03,100,0.1,0.1,0.8,1.0,10,5,0.9862816572762855,0.8494531720877854,0.8062811698729515,0.7834055481958557,0.9607205770272069,0.9929769287184236,0.9964502905368763,0.9987795037224373,0.9872419067787573,0.7059294154571474,0.6161120492090266,0.5680315926692738,0.9341992472756562,91.8,5401.0,6.6,69.8
lightgbm,yeo_johnson,class_weight,dart,0.03,100,0.1,0.1,1.0,0.8,10,5,0.9868203342883293,0.8567123113396862,0.81462313212734,0.7920698521104123,0.9624609919498163,0.993251161582376,0.9965828464697711,0.9988164928106563,0.9877478834902378,0.7201734610969963,0.632663417784909,0.585323211410168,0.9371741004093945,94.6,5401.2,6.4,67.0
lightgbm,yeo_johnson,class_weight,dart,0.03,100,0.1,0.1,1.0,1.0,10,5,0.9863893900996572,0.8516614690849792,0.8095557057072785,0.7870576057488851,0.9579981706050438,0.9930309459196085,0.9964058116257544,0.9986685432974787,0.9874568744274971,0.7102919922503499,0.6227055997888027,0.5754466682002913,0.9285394667825905,93.0,5400.4,7.2,68.6
lightgbm,yeo_johnson,class_weight,dart,0.05,100,0.1,0.1,0.8,0.8,10,5,0.9872871464341749,0.8647825564498515,0.8265211036628628,0.8055143377206931,0.9560208920519685,0.9934866215661462,0.9964787449584944,0.998483625215177,0.9885398629294059,0.7360784913335567,0.6565634623672312,0.6125450502262096,0.9235019211745309,99.0,5399.4,8.2,62.6
lightgbm,yeo_johnson,class_weight,dart,0.05,100,0.1,0.1,0.8,1.0,10,5,0.9872512526866313,0.864242117798965,0.8255296821802522,0.8043135971909393,0.9568082528265623,0.9934684617996755,0.9964935591422892,0.9985206074636975,0.9884674795460324,0.7350157737982544,0.6545658052182155,0.6101065869181811,0.9251490261070924,98.6,5399.6,8.0,63.0
lightgbm,yeo_johnson,class_weight,dart,0.05,100,0.1,0.1,1.0,0.8,10,5,0.9875385574092025,0.867739957700573,0.8292362302142348,0.808058120624818,0.9590571825576781,0.9936151348160316,0.9965968036054408,0.9985945788004372,0.9886852713014047,0.7418647805851146,0.6618756568230287,0.6175216624491988,0.9294290938139514,99.8,5400.0,7.6,61.8
lightgbm,yeo_johnson,class_weight,dart,0.05,100,0.1,0.1,1.0,1.0,10,5,0.9878617429841328,0.8718918805916948,0.8343953837040834,0.8136405111931599,0.9597257588431093,0.9937798152798208,0.9966630340635951,0.998594571960739,0.9890116522948971,0.7500039459035688,0.6721277333445717,0.6286864504255809,0.9304398653913216,101.6,5400.0,7.6,60.0
lightgbm,yeo_johnson,class_weight,dart,0.1,100,0.1,0.1,0.8,0.8,10,5,0.9875744511567464,0.8694904253032434,0.8330898747073772,0.8128784428659526,0.9543165483350643,0.9936317630222906,0.9964486981215537,0.9983356757019992,0.9889726061606664,0.7453490875841962,0.6697310512932007,0.6274212100299057,0.9196604905094622,101.4,5398.6,9.0,60.2
lightgbm,yeo_johnson,class_weight,dart,0.1,100,0.1,0.1,0.8,1.0,10,5,0.9877540230559461,0.8705274306318904,0.8326760658152557,0.8117771571558929,0.9596652466682235,0.993724889952756,0.9966409360538494,0.998594571960739,0.9889028548898798,0.7473299713110246,0.6687111955766621,0.6249597423510467,0.930427638446567,101.0,5400.0,7.6,60.6
lightgbm,yeo_johnson,class_weight,dart,0.1,100,0.1,0.1,1.0,0.8,10,5,0.9878976496268617,0.8740424916234224,0.8391944377539111,0.819654574765656,0.9536373604366759,0.9937959382660164,0.9964704965030846,0.9982616838461646,0.9893706349659366,0.7542890449808285,0.681918379004738,0.6410474656851469,0.9179040859074152,103.6,5398.2,9.4,58.0
lightgbm,yeo_johnson,class_weight,dart,0.1,100,0.1,0.1,1.0,1.0,10,5,0.98804128264537,0.8754763636546178,0.8405284491350876,0.8209184704711975,0.9551236805467838,0.9938696715557122,0.9965444000780066,0.9983356757019992,0.9894439864987652,0.7570830557535235,0.6845124981921686,0.6435012652403957,0.9208033745948025,104.0,5398.6,9.0,57.6
1 model scaling_method sampling_method boosting_type learning_rate num_leaves l2_reg l1_reg tree_subsample subsample k_neighbors kmeans_estimator accuracy f1_macro f2_macro recall_macro precision_macro f1_class0 f2_class0 recall_class0 precision_class0 f1_class1 f2_class1 recall_class1 precision_class1 TP TN FP FN
2 lightgbm standard_scaling KMeansSMOTE gbdt 0.03 100 0.1 0.1 0.8 0.8 10 5 0.9872871528817676 0.8635827226233982 0.82363889290808 0.8019139194786398 0.9607516165514827 0.9934880771322151 0.9966119285054977 0.9987055323856977 0.9883253364382266 0.7336773681145814 0.6506658573106621 0.605122306571582 0.9331778966647388 97.8 5400.6 7.0 63.8
3 lightgbm standard_scaling KMeansSMOTE gbdt 0.03 100 0.1 0.1 0.8 1.0 10 5 0.9873948857051393 0.8660654376430875 0.8278053873421346 0.8067827329934085 0.9571174874451384 0.9935416656406844 0.9965229981920896 0.9985206074636975 0.9886125885446072 0.7385892096454906 0.6590877764921795 0.6150448585231194 0.9256223863456696 99.4 5399.6 8.0 62.2
4 lightgbm standard_scaling KMeansSMOTE gbdt 0.03 100 0.1 0.1 1.0 0.8 10 5 0.9872871270913975 0.8636064344844797 0.8240482741313839 0.8025088748233425 0.9596889613355953 0.9934880088638021 0.9965897765183312 0.9986685432974788 0.9883616207181698 0.7337248601051574 0.6515067717444367 0.6063492063492064 0.9310163019530208 98.0 5400.4 7.2 63.6
5 lightgbm standard_scaling KMeansSMOTE gbdt 0.03 100 0.1 0.1 1.0 1.0 10 5 0.9871434811777045 0.863449771888091 0.8256341947711338 0.8048454076194018 0.9533828520533423 0.9934127773860718 0.9963827135574705 0.9983726647902182 0.9885024551041862 0.7334867663901103 0.6548856759847967 0.6113181504485852 0.9182632490024984 98.8 5398.8 8.8 62.8
6 lightgbm standard_scaling KMeansSMOTE gbdt 0.05 100 0.1 0.1 0.8 0.8 10 5 0.987358998405188 0.8658212328853709 0.8277045950376559 0.8067604078073429 0.9566247977036533 0.9935230811207543 0.9964934015324072 0.998483625215177 0.9886121814446716 0.7381193846499876 0.6589157885429044 0.6150371903995093 0.9246374139626351 99.4 5399.4 8.2 62.2
7 lightgbm standard_scaling KMeansSMOTE gbdt 0.05 100 0.1 0.1 0.8 1.0 10 5 0.9876462902325743 0.8702280820945317 0.8337605692544805 0.8135218826445911 0.9553624954113376 0.9936685848798849 0.9964855981899191 0.9983726511108216 0.9890093876458101 0.7467875793091783 0.6710355403190421 0.6286711141783605 0.9217156031768651 101.6 5398.8 8.8 60.0
8 lightgbm standard_scaling KMeansSMOTE gbdt 0.05 100 0.1 0.1 1.0 0.8 10 5 0.9876103706946605 0.8685306532899453 0.8303217288431941 0.8092965157481593 0.9592309417836985 0.9936518407305781 0.9966115244112512 0.9985945651210407 0.9887583083957047 0.7434094658493124 0.6640319332751371 0.619998466375278 0.9297035751716924 100.2 5400.0 7.6 61.4
9 lightgbm standard_scaling KMeansSMOTE gbdt 0.05 100 0.1 0.1 1.0 1.0 10 5 0.9875744640519315 0.8697383859570031 0.8335824387321773 0.8134887344578754 0.9538702717550708 0.993631447606049 0.9964264483135749 0.9982986866137802 0.9890083881850659 0.7458453243079577 0.6707384291507799 0.6286787823019708 0.9187321553250758 101.6 5398.4 9.2 60.0
10 lightgbm standard_scaling KMeansSMOTE gbdt 0.1 100 0.1 0.1 0.8 0.8 10 5 0.9876463031277595 0.8696452935291772 0.832345622530247 0.8117216735235644 0.9573224840422384 0.9936693371232203 0.99655223796351 0.9984836046960821 0.988901862104868 0.7456212499351342 0.6681390070969839 0.6249597423510467 0.9257431059796088 101.0 5399.4 8.2 60.6
11 lightgbm standard_scaling KMeansSMOTE gbdt 0.1 100 0.1 0.1 0.8 1.0 10 5 0.9879335756123682 0.8735142470990855 0.8373885067575266 0.81727407420208 0.9572776440521376 0.9938154842661611 0.9965889028545231 0.9984466224475614 0.9892278794141068 0.7532130099320097 0.6781881106605302 0.6361015259565985 0.9253274086901682 102.8 5399.2 8.4 58.8
12 lightgbm standard_scaling KMeansSMOTE gbdt 0.1 100 0.1 0.1 1.0 0.8 10 5 0.9874667118857822 0.8680481539924493 0.8317553825774301 0.8116330485442184 0.9529694195282257 0.993576898041232 0.9964044843956639 0.9982986866137802 0.9889004742854578 0.7425194099436665 0.6671062807591965 0.6249674104746569 0.9170383647709939 101.0 5398.4 9.2 60.6
13 lightgbm standard_scaling KMeansSMOTE gbdt 0.1 100 0.1 0.1 1.0 1.0 10 5 0.9879694887026897 0.8749776173022346 0.840353596310616 0.8209121572972682 0.9537927663883565 0.9938325369626163 0.9964852212462812 0.9982617043652595 0.9894432107974284 0.7561226976418527 0.6842219713749508 0.6435626102292769 0.9181423219792844 104.0 5398.2 9.4 57.6
14 lightgbm standard_scaling KMeansSMOTE dart 0.03 100 0.1 0.1 0.8 0.8 10 5 0.9864612162803 0.8517213882755159 0.8087404085309796 0.785885510418824 0.9614784852134713 0.9930685455041811 0.9965093140147973 0.9988164928106563 0.9873866482719492 0.7103742310468509 0.620971503047162 0.5729545280269918 0.9355703221549934 92.6 5401.2 6.4 69.0
15 lightgbm standard_scaling KMeansSMOTE dart 0.03 100 0.1 0.1 0.8 1.0 10 5 0.9862816572762855 0.8494531720877854 0.8062811698729515 0.7834055481958557 0.9607205770272069 0.9929769287184236 0.9964502905368763 0.9987795037224373 0.9872419067787573 0.7059294154571474 0.6161120492090266 0.5680315926692738 0.9341992472756562 91.8 5401.0 6.6 69.8
16 lightgbm standard_scaling KMeansSMOTE dart 0.03 100 0.1 0.1 1.0 0.8 10 5 0.9868203342883293 0.8567123113396862 0.81462313212734 0.7920698521104123 0.9624609919498163 0.993251161582376 0.9965828464697711 0.9988164928106563 0.9877478834902378 0.7201734610969963 0.632663417784909 0.585323211410168 0.9371741004093945 94.6 5401.2 6.4 67.0
17 lightgbm standard_scaling KMeansSMOTE dart 0.03 100 0.1 0.1 1.0 1.0 10 5 0.9863893900996572 0.8516614690849792 0.8095557057072785 0.7870576057488851 0.9579981706050438 0.9930309459196085 0.9964058116257544 0.9986685432974787 0.9874568744274971 0.7102919922503499 0.6227055997888027 0.5754466682002913 0.9285394667825905 93.0 5400.4 7.2 68.6
18 lightgbm standard_scaling KMeansSMOTE dart 0.05 100 0.1 0.1 0.8 0.8 10 5 0.9872871464341749 0.8647825564498515 0.8265211036628628 0.8055143377206931 0.9560208920519685 0.9934866215661462 0.9964787449584944 0.998483625215177 0.9885398629294059 0.7360784913335567 0.6565634623672312 0.6125450502262096 0.9235019211745309 99.0 5399.4 8.2 62.6
19 lightgbm standard_scaling KMeansSMOTE dart 0.05 100 0.1 0.1 0.8 1.0 10 5 0.9872512526866313 0.864242117798965 0.8255296821802522 0.8043135971909393 0.9568082528265623 0.9934684617996755 0.9964935591422892 0.9985206074636975 0.9884674795460324 0.7350157737982544 0.6545658052182155 0.6101065869181811 0.9251490261070924 98.6 5399.6 8.0 63.0
20 lightgbm standard_scaling KMeansSMOTE dart 0.05 100 0.1 0.1 1.0 0.8 10 5 0.9875385574092025 0.867739957700573 0.8292362302142348 0.808058120624818 0.9590571825576781 0.9936151348160316 0.9965968036054408 0.9985945788004372 0.9886852713014047 0.7418647805851146 0.6618756568230287 0.6175216624491988 0.9294290938139514 99.8 5400.0 7.6 61.8
21 lightgbm standard_scaling KMeansSMOTE dart 0.05 100 0.1 0.1 1.0 1.0 10 5 0.9878617429841328 0.8718918805916948 0.8343953837040834 0.8136405111931599 0.9597257588431093 0.9937798152798208 0.9966630340635951 0.998594571960739 0.9890116522948971 0.7500039459035688 0.6721277333445717 0.6286864504255809 0.9304398653913216 101.6 5400.0 7.6 60.0
22 lightgbm standard_scaling KMeansSMOTE dart 0.1 100 0.1 0.1 0.8 0.8 10 5 0.9875744511567464 0.8694904253032434 0.8330898747073772 0.8128784428659526 0.9543165483350643 0.9936317630222906 0.9964486981215537 0.9983356757019992 0.9889726061606664 0.7453490875841962 0.6697310512932007 0.6274212100299057 0.9196604905094622 101.4 5398.6 9.0 60.2
23 lightgbm standard_scaling KMeansSMOTE dart 0.1 100 0.1 0.1 0.8 1.0 10 5 0.9877540230559461 0.8705274306318904 0.8326760658152557 0.8117771571558929 0.9596652466682235 0.993724889952756 0.9966409360538494 0.998594571960739 0.9889028548898798 0.7473299713110246 0.6687111955766621 0.6249597423510467 0.930427638446567 101.0 5400.0 7.6 60.6
24 lightgbm standard_scaling KMeansSMOTE dart 0.1 100 0.1 0.1 1.0 0.8 10 5 0.9878976496268617 0.8740424916234224 0.8391944377539111 0.819654574765656 0.9536373604366759 0.9937959382660164 0.9964704965030846 0.9982616838461646 0.9893706349659366 0.7542890449808285 0.681918379004738 0.6410474656851469 0.9179040859074152 103.6 5398.2 9.4 58.0
25 lightgbm standard_scaling KMeansSMOTE dart 0.1 100 0.1 0.1 1.0 1.0 10 5 0.98804128264537 0.8754763636546178 0.8405284491350876 0.8209184704711975 0.9551236805467838 0.9938696715557122 0.9965444000780066 0.9983356757019992 0.9894439864987652 0.7570830557535235 0.6845124981921686 0.6435012652403957 0.9208033745948025 104.0 5398.6 9.0 57.6
26 lightgbm standard_scaling class_weight gbdt 0.03 100 0.1 0.1 0.8 0.8 10 5 0.9872871528817676 0.8635827226233982 0.82363889290808 0.8019139194786398 0.9607516165514827 0.9934880771322151 0.9966119285054977 0.9987055323856977 0.9883253364382266 0.7336773681145814 0.6506658573106621 0.605122306571582 0.9331778966647388 97.8 5400.6 7.0 63.8
27 lightgbm standard_scaling class_weight gbdt 0.03 100 0.1 0.1 0.8 1.0 10 5 0.9873948857051393 0.8660654376430875 0.8278053873421346 0.8067827329934085 0.9571174874451384 0.9935416656406844 0.9965229981920896 0.9985206074636975 0.9886125885446072 0.7385892096454906 0.6590877764921795 0.6150448585231194 0.9256223863456696 99.4 5399.6 8.0 62.2
28 lightgbm standard_scaling class_weight gbdt 0.03 100 0.1 0.1 1.0 0.8 10 5 0.9872871270913975 0.8636064344844797 0.8240482741313839 0.8025088748233425 0.9596889613355953 0.9934880088638021 0.9965897765183312 0.9986685432974788 0.9883616207181698 0.7337248601051574 0.6515067717444367 0.6063492063492064 0.9310163019530208 98.0 5400.4 7.2 63.6
29 lightgbm standard_scaling class_weight gbdt 0.03 100 0.1 0.1 1.0 1.0 10 5 0.9871434811777045 0.863449771888091 0.8256341947711338 0.8048454076194018 0.9533828520533423 0.9934127773860718 0.9963827135574705 0.9983726647902182 0.9885024551041862 0.7334867663901103 0.6548856759847967 0.6113181504485852 0.9182632490024984 98.8 5398.8 8.8 62.8
30 lightgbm standard_scaling class_weight gbdt 0.05 100 0.1 0.1 0.8 0.8 10 5 0.987358998405188 0.8658212328853709 0.8277045950376559 0.8067604078073429 0.9566247977036533 0.9935230811207543 0.9964934015324072 0.998483625215177 0.9886121814446716 0.7381193846499876 0.6589157885429044 0.6150371903995093 0.9246374139626351 99.4 5399.4 8.2 62.2
31 lightgbm standard_scaling class_weight gbdt 0.05 100 0.1 0.1 0.8 1.0 10 5 0.9876462902325743 0.8702280820945317 0.8337605692544805 0.8135218826445911 0.9553624954113376 0.9936685848798849 0.9964855981899191 0.9983726511108216 0.9890093876458101 0.7467875793091783 0.6710355403190421 0.6286711141783605 0.9217156031768651 101.6 5398.8 8.8 60.0
32 lightgbm standard_scaling class_weight gbdt 0.05 100 0.1 0.1 1.0 0.8 10 5 0.9876103706946605 0.8685306532899453 0.8303217288431941 0.8092965157481593 0.9592309417836985 0.9936518407305781 0.9966115244112512 0.9985945651210407 0.9887583083957047 0.7434094658493124 0.6640319332751371 0.619998466375278 0.9297035751716924 100.2 5400.0 7.6 61.4
33 lightgbm standard_scaling class_weight gbdt 0.05 100 0.1 0.1 1.0 1.0 10 5 0.9875744640519315 0.8697383859570031 0.8335824387321773 0.8134887344578754 0.9538702717550708 0.993631447606049 0.9964264483135749 0.9982986866137802 0.9890083881850659 0.7458453243079577 0.6707384291507799 0.6286787823019708 0.9187321553250758 101.6 5398.4 9.2 60.0
34 lightgbm standard_scaling class_weight gbdt 0.1 100 0.1 0.1 0.8 0.8 10 5 0.9876463031277595 0.8696452935291772 0.832345622530247 0.8117216735235644 0.9573224840422384 0.9936693371232203 0.99655223796351 0.9984836046960821 0.988901862104868 0.7456212499351342 0.6681390070969839 0.6249597423510467 0.9257431059796088 101.0 5399.4 8.2 60.6
35 lightgbm standard_scaling class_weight gbdt 0.1 100 0.1 0.1 0.8 1.0 10 5 0.9879335756123682 0.8735142470990855 0.8373885067575266 0.81727407420208 0.9572776440521376 0.9938154842661611 0.9965889028545231 0.9984466224475614 0.9892278794141068 0.7532130099320097 0.6781881106605302 0.6361015259565985 0.9253274086901682 102.8 5399.2 8.4 58.8
36 lightgbm standard_scaling class_weight gbdt 0.1 100 0.1 0.1 1.0 0.8 10 5 0.9874667118857822 0.8680481539924493 0.8317553825774301 0.8116330485442184 0.9529694195282257 0.993576898041232 0.9964044843956639 0.9982986866137802 0.9889004742854578 0.7425194099436665 0.6671062807591965 0.6249674104746569 0.9170383647709939 101.0 5398.4 9.2 60.6
37 lightgbm standard_scaling class_weight gbdt 0.1 100 0.1 0.1 1.0 1.0 10 5 0.9879694887026897 0.8749776173022346 0.840353596310616 0.8209121572972682 0.9537927663883565 0.9938325369626163 0.9964852212462812 0.9982617043652595 0.9894432107974284 0.7561226976418527 0.6842219713749508 0.6435626102292769 0.9181423219792844 104.0 5398.2 9.4 57.6
38 lightgbm standard_scaling class_weight dart 0.03 100 0.1 0.1 0.8 0.8 10 5 0.9864612162803 0.8517213882755159 0.8087404085309796 0.785885510418824 0.9614784852134713 0.9930685455041811 0.9965093140147973 0.9988164928106563 0.9873866482719492 0.7103742310468509 0.620971503047162 0.5729545280269918 0.9355703221549934 92.6 5401.2 6.4 69.0
39 lightgbm standard_scaling class_weight dart 0.03 100 0.1 0.1 0.8 1.0 10 5 0.9862816572762855 0.8494531720877854 0.8062811698729515 0.7834055481958557 0.9607205770272069 0.9929769287184236 0.9964502905368763 0.9987795037224373 0.9872419067787573 0.7059294154571474 0.6161120492090266 0.5680315926692738 0.9341992472756562 91.8 5401.0 6.6 69.8
40 lightgbm standard_scaling class_weight dart 0.03 100 0.1 0.1 1.0 0.8 10 5 0.9868203342883293 0.8567123113396862 0.81462313212734 0.7920698521104123 0.9624609919498163 0.993251161582376 0.9965828464697711 0.9988164928106563 0.9877478834902378 0.7201734610969963 0.632663417784909 0.585323211410168 0.9371741004093945 94.6 5401.2 6.4 67.0
41 lightgbm standard_scaling class_weight dart 0.03 100 0.1 0.1 1.0 1.0 10 5 0.9863893900996572 0.8516614690849792 0.8095557057072785 0.7870576057488851 0.9579981706050438 0.9930309459196085 0.9964058116257544 0.9986685432974787 0.9874568744274971 0.7102919922503499 0.6227055997888027 0.5754466682002913 0.9285394667825905 93.0 5400.4 7.2 68.6
42 lightgbm standard_scaling class_weight dart 0.05 100 0.1 0.1 0.8 0.8 10 5 0.9872871464341749 0.8647825564498515 0.8265211036628628 0.8055143377206931 0.9560208920519685 0.9934866215661462 0.9964787449584944 0.998483625215177 0.9885398629294059 0.7360784913335567 0.6565634623672312 0.6125450502262096 0.9235019211745309 99.0 5399.4 8.2 62.6
43 lightgbm standard_scaling class_weight dart 0.05 100 0.1 0.1 0.8 1.0 10 5 0.9872512526866313 0.864242117798965 0.8255296821802522 0.8043135971909393 0.9568082528265623 0.9934684617996755 0.9964935591422892 0.9985206074636975 0.9884674795460324 0.7350157737982544 0.6545658052182155 0.6101065869181811 0.9251490261070924 98.6 5399.6 8.0 63.0
44 lightgbm standard_scaling class_weight dart 0.05 100 0.1 0.1 1.0 0.8 10 5 0.9875385574092025 0.867739957700573 0.8292362302142348 0.808058120624818 0.9590571825576781 0.9936151348160316 0.9965968036054408 0.9985945788004372 0.9886852713014047 0.7418647805851146 0.6618756568230287 0.6175216624491988 0.9294290938139514 99.8 5400.0 7.6 61.8
45 lightgbm standard_scaling class_weight dart 0.05 100 0.1 0.1 1.0 1.0 10 5 0.9878617429841328 0.8718918805916948 0.8343953837040834 0.8136405111931599 0.9597257588431093 0.9937798152798208 0.9966630340635951 0.998594571960739 0.9890116522948971 0.7500039459035688 0.6721277333445717 0.6286864504255809 0.9304398653913216 101.6 5400.0 7.6 60.0
46 lightgbm standard_scaling class_weight dart 0.1 100 0.1 0.1 0.8 0.8 10 5 0.9875744511567464 0.8694904253032434 0.8330898747073772 0.8128784428659526 0.9543165483350643 0.9936317630222906 0.9964486981215537 0.9983356757019992 0.9889726061606664 0.7453490875841962 0.6697310512932007 0.6274212100299057 0.9196604905094622 101.4 5398.6 9.0 60.2
47 lightgbm standard_scaling class_weight dart 0.1 100 0.1 0.1 0.8 1.0 10 5 0.9877540230559461 0.8705274306318904 0.8326760658152557 0.8117771571558929 0.9596652466682235 0.993724889952756 0.9966409360538494 0.998594571960739 0.9889028548898798 0.7473299713110246 0.6687111955766621 0.6249597423510467 0.930427638446567 101.0 5400.0 7.6 60.6
48 lightgbm standard_scaling class_weight dart 0.1 100 0.1 0.1 1.0 0.8 10 5 0.9878976496268617 0.8740424916234224 0.8391944377539111 0.819654574765656 0.9536373604366759 0.9937959382660164 0.9964704965030846 0.9982616838461646 0.9893706349659366 0.7542890449808285 0.681918379004738 0.6410474656851469 0.9179040859074152 103.6 5398.2 9.4 58.0
49 lightgbm standard_scaling class_weight dart 0.1 100 0.1 0.1 1.0 1.0 10 5 0.98804128264537 0.8754763636546178 0.8405284491350876 0.8209184704711975 0.9551236805467838 0.9938696715557122 0.9965444000780066 0.9983356757019992 0.9894439864987652 0.7570830557535235 0.6845124981921686 0.6435012652403957 0.9208033745948025 104.0 5398.6 9.0 57.6
50 lightgbm robust_scaling KMeansSMOTE gbdt 0.03 100 0.1 0.1 0.8 0.8 10 5 0.9872871528817676 0.8635827226233982 0.82363889290808 0.8019139194786398 0.9607516165514827 0.9934880771322151 0.9966119285054977 0.9987055323856977 0.9883253364382266 0.7336773681145814 0.6506658573106621 0.605122306571582 0.9331778966647388 97.8 5400.6 7.0 63.8
51 lightgbm robust_scaling KMeansSMOTE gbdt 0.03 100 0.1 0.1 0.8 1.0 10 5 0.9873948857051393 0.8660654376430875 0.8278053873421346 0.8067827329934085 0.9571174874451384 0.9935416656406844 0.9965229981920896 0.9985206074636975 0.9886125885446072 0.7385892096454906 0.6590877764921795 0.6150448585231194 0.9256223863456696 99.4 5399.6 8.0 62.2
52 lightgbm robust_scaling KMeansSMOTE gbdt 0.03 100 0.1 0.1 1.0 0.8 10 5 0.9872871270913975 0.8636064344844797 0.8240482741313839 0.8025088748233425 0.9596889613355953 0.9934880088638021 0.9965897765183312 0.9986685432974788 0.9883616207181698 0.7337248601051574 0.6515067717444367 0.6063492063492064 0.9310163019530208 98.0 5400.4 7.2 63.6
53 lightgbm robust_scaling KMeansSMOTE gbdt 0.03 100 0.1 0.1 1.0 1.0 10 5 0.9871434811777045 0.863449771888091 0.8256341947711338 0.8048454076194018 0.9533828520533423 0.9934127773860718 0.9963827135574705 0.9983726647902182 0.9885024551041862 0.7334867663901103 0.6548856759847967 0.6113181504485852 0.9182632490024984 98.8 5398.8 8.8 62.8
54 lightgbm robust_scaling KMeansSMOTE gbdt 0.05 100 0.1 0.1 0.8 0.8 10 5 0.987358998405188 0.8658212328853709 0.8277045950376559 0.8067604078073429 0.9566247977036533 0.9935230811207543 0.9964934015324072 0.998483625215177 0.9886121814446716 0.7381193846499876 0.6589157885429044 0.6150371903995093 0.9246374139626351 99.4 5399.4 8.2 62.2
55 lightgbm robust_scaling KMeansSMOTE gbdt 0.05 100 0.1 0.1 0.8 1.0 10 5 0.9876462902325743 0.8702280820945317 0.8337605692544805 0.8135218826445911 0.9553624954113376 0.9936685848798849 0.9964855981899191 0.9983726511108216 0.9890093876458101 0.7467875793091783 0.6710355403190421 0.6286711141783605 0.9217156031768651 101.6 5398.8 8.8 60.0
56 lightgbm robust_scaling KMeansSMOTE gbdt 0.05 100 0.1 0.1 1.0 0.8 10 5 0.9876103706946605 0.8685306532899453 0.8303217288431941 0.8092965157481593 0.9592309417836985 0.9936518407305781 0.9966115244112512 0.9985945651210407 0.9887583083957047 0.7434094658493124 0.6640319332751371 0.619998466375278 0.9297035751716924 100.2 5400.0 7.6 61.4
57 lightgbm robust_scaling KMeansSMOTE gbdt 0.05 100 0.1 0.1 1.0 1.0 10 5 0.9875744640519315 0.8697383859570031 0.8335824387321773 0.8134887344578754 0.9538702717550708 0.993631447606049 0.9964264483135749 0.9982986866137802 0.9890083881850659 0.7458453243079577 0.6707384291507799 0.6286787823019708 0.9187321553250758 101.6 5398.4 9.2 60.0
58 lightgbm robust_scaling KMeansSMOTE gbdt 0.1 100 0.1 0.1 0.8 0.8 10 5 0.9876463031277595 0.8696452935291772 0.832345622530247 0.8117216735235644 0.9573224840422384 0.9936693371232203 0.99655223796351 0.9984836046960821 0.988901862104868 0.7456212499351342 0.6681390070969839 0.6249597423510467 0.9257431059796088 101.0 5399.4 8.2 60.6
59 lightgbm robust_scaling KMeansSMOTE gbdt 0.1 100 0.1 0.1 0.8 1.0 10 5 0.9879335756123682 0.8735142470990855 0.8373885067575266 0.81727407420208 0.9572776440521376 0.9938154842661611 0.9965889028545231 0.9984466224475614 0.9892278794141068 0.7532130099320097 0.6781881106605302 0.6361015259565985 0.9253274086901682 102.8 5399.2 8.4 58.8
60 lightgbm robust_scaling KMeansSMOTE gbdt 0.1 100 0.1 0.1 1.0 0.8 10 5 0.9874667118857822 0.8680481539924493 0.8317553825774301 0.8116330485442184 0.9529694195282257 0.993576898041232 0.9964044843956639 0.9982986866137802 0.9889004742854578 0.7425194099436665 0.6671062807591965 0.6249674104746569 0.9170383647709939 101.0 5398.4 9.2 60.6
61 lightgbm robust_scaling KMeansSMOTE gbdt 0.1 100 0.1 0.1 1.0 1.0 10 5 0.9879694887026897 0.8749776173022346 0.840353596310616 0.8209121572972682 0.9537927663883565 0.9938325369626163 0.9964852212462812 0.9982617043652595 0.9894432107974284 0.7561226976418527 0.6842219713749508 0.6435626102292769 0.9181423219792844 104.0 5398.2 9.4 57.6
62 lightgbm robust_scaling KMeansSMOTE dart 0.03 100 0.1 0.1 0.8 0.8 10 5 0.9864612162803 0.8517213882755159 0.8087404085309796 0.785885510418824 0.9614784852134713 0.9930685455041811 0.9965093140147973 0.9988164928106563 0.9873866482719492 0.7103742310468509 0.620971503047162 0.5729545280269918 0.9355703221549934 92.6 5401.2 6.4 69.0
63 lightgbm robust_scaling KMeansSMOTE dart 0.03 100 0.1 0.1 0.8 1.0 10 5 0.9862816572762855 0.8494531720877854 0.8062811698729515 0.7834055481958557 0.9607205770272069 0.9929769287184236 0.9964502905368763 0.9987795037224373 0.9872419067787573 0.7059294154571474 0.6161120492090266 0.5680315926692738 0.9341992472756562 91.8 5401.0 6.6 69.8
64 lightgbm robust_scaling KMeansSMOTE dart 0.03 100 0.1 0.1 1.0 0.8 10 5 0.9868203342883293 0.8567123113396862 0.81462313212734 0.7920698521104123 0.9624609919498163 0.993251161582376 0.9965828464697711 0.9988164928106563 0.9877478834902378 0.7201734610969963 0.632663417784909 0.585323211410168 0.9371741004093945 94.6 5401.2 6.4 67.0
65 lightgbm robust_scaling KMeansSMOTE dart 0.03 100 0.1 0.1 1.0 1.0 10 5 0.9863893900996572 0.8516614690849792 0.8095557057072785 0.7870576057488851 0.9579981706050438 0.9930309459196085 0.9964058116257544 0.9986685432974787 0.9874568744274971 0.7102919922503499 0.6227055997888027 0.5754466682002913 0.9285394667825905 93.0 5400.4 7.2 68.6
66 lightgbm robust_scaling KMeansSMOTE dart 0.05 100 0.1 0.1 0.8 0.8 10 5 0.9872871464341749 0.8647825564498515 0.8265211036628628 0.8055143377206931 0.9560208920519685 0.9934866215661462 0.9964787449584944 0.998483625215177 0.9885398629294059 0.7360784913335567 0.6565634623672312 0.6125450502262096 0.9235019211745309 99.0 5399.4 8.2 62.6
67 lightgbm robust_scaling KMeansSMOTE dart 0.05 100 0.1 0.1 0.8 1.0 10 5 0.9872512526866313 0.864242117798965 0.8255296821802522 0.8043135971909393 0.9568082528265623 0.9934684617996755 0.9964935591422892 0.9985206074636975 0.9884674795460324 0.7350157737982544 0.6545658052182155 0.6101065869181811 0.9251490261070924 98.6 5399.6 8.0 63.0
68 lightgbm robust_scaling KMeansSMOTE dart 0.05 100 0.1 0.1 1.0 0.8 10 5 0.9875385574092025 0.867739957700573 0.8292362302142348 0.808058120624818 0.9590571825576781 0.9936151348160316 0.9965968036054408 0.9985945788004372 0.9886852713014047 0.7418647805851146 0.6618756568230287 0.6175216624491988 0.9294290938139514 99.8 5400.0 7.6 61.8
69 lightgbm robust_scaling KMeansSMOTE dart 0.05 100 0.1 0.1 1.0 1.0 10 5 0.9878617429841328 0.8718918805916948 0.8343953837040834 0.8136405111931599 0.9597257588431093 0.9937798152798208 0.9966630340635951 0.998594571960739 0.9890116522948971 0.7500039459035688 0.6721277333445717 0.6286864504255809 0.9304398653913216 101.6 5400.0 7.6 60.0
70 lightgbm robust_scaling KMeansSMOTE dart 0.1 100 0.1 0.1 0.8 0.8 10 5 0.9875744511567464 0.8694904253032434 0.8330898747073772 0.8128784428659526 0.9543165483350643 0.9936317630222906 0.9964486981215537 0.9983356757019992 0.9889726061606664 0.7453490875841962 0.6697310512932007 0.6274212100299057 0.9196604905094622 101.4 5398.6 9.0 60.2
71 lightgbm robust_scaling KMeansSMOTE dart 0.1 100 0.1 0.1 0.8 1.0 10 5 0.9877540230559461 0.8705274306318904 0.8326760658152557 0.8117771571558929 0.9596652466682235 0.993724889952756 0.9966409360538494 0.998594571960739 0.9889028548898798 0.7473299713110246 0.6687111955766621 0.6249597423510467 0.930427638446567 101.0 5400.0 7.6 60.6
72 lightgbm robust_scaling KMeansSMOTE dart 0.1 100 0.1 0.1 1.0 0.8 10 5 0.9878976496268617 0.8740424916234224 0.8391944377539111 0.819654574765656 0.9536373604366759 0.9937959382660164 0.9964704965030846 0.9982616838461646 0.9893706349659366 0.7542890449808285 0.681918379004738 0.6410474656851469 0.9179040859074152 103.6 5398.2 9.4 58.0
73 lightgbm robust_scaling KMeansSMOTE dart 0.1 100 0.1 0.1 1.0 1.0 10 5 0.98804128264537 0.8754763636546178 0.8405284491350876 0.8209184704711975 0.9551236805467838 0.9938696715557122 0.9965444000780066 0.9983356757019992 0.9894439864987652 0.7570830557535235 0.6845124981921686 0.6435012652403957 0.9208033745948025 104.0 5398.6 9.0 57.6
74 lightgbm robust_scaling class_weight gbdt 0.03 100 0.1 0.1 0.8 0.8 10 5 0.9872871528817676 0.8635827226233982 0.82363889290808 0.8019139194786398 0.9607516165514827 0.9934880771322151 0.9966119285054977 0.9987055323856977 0.9883253364382266 0.7336773681145814 0.6506658573106621 0.605122306571582 0.9331778966647388 97.8 5400.6 7.0 63.8
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90 lightgbm robust_scaling class_weight dart 0.05 100 0.1 0.1 0.8 0.8 10 5 0.9872871464341749 0.8647825564498515 0.8265211036628628 0.8055143377206931 0.9560208920519685 0.9934866215661462 0.9964787449584944 0.998483625215177 0.9885398629294059 0.7360784913335567 0.6565634623672312 0.6125450502262096 0.9235019211745309 99.0 5399.4 8.2 62.6
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96 lightgbm robust_scaling class_weight dart 0.1 100 0.1 0.1 1.0 0.8 10 5 0.9878976496268617 0.8740424916234224 0.8391944377539111 0.819654574765656 0.9536373604366759 0.9937959382660164 0.9964704965030846 0.9982616838461646 0.9893706349659366 0.7542890449808285 0.681918379004738 0.6410474656851469 0.9179040859074152 103.6 5398.2 9.4 58.0
97 lightgbm robust_scaling class_weight dart 0.1 100 0.1 0.1 1.0 1.0 10 5 0.98804128264537 0.8754763636546178 0.8405284491350876 0.8209184704711975 0.9551236805467838 0.9938696715557122 0.9965444000780066 0.9983356757019992 0.9894439864987652 0.7570830557535235 0.6845124981921686 0.6435012652403957 0.9208033745948025 104.0 5398.6 9.0 57.6
98 lightgbm minmax_scaling KMeansSMOTE gbdt 0.03 100 0.1 0.1 0.8 0.8 10 5 0.9872871528817676 0.8635827226233982 0.82363889290808 0.8019139194786398 0.9607516165514827 0.9934880771322151 0.9966119285054977 0.9987055323856977 0.9883253364382266 0.7336773681145814 0.6506658573106621 0.605122306571582 0.9331778966647388 97.8 5400.6 7.0 63.8
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103 lightgbm minmax_scaling KMeansSMOTE gbdt 0.05 100 0.1 0.1 0.8 1.0 10 5 0.9876462902325743 0.8702280820945317 0.8337605692544805 0.8135218826445911 0.9553624954113376 0.9936685848798849 0.9964855981899191 0.9983726511108216 0.9890093876458101 0.7467875793091783 0.6710355403190421 0.6286711141783605 0.9217156031768651 101.6 5398.8 8.8 60.0
104 lightgbm minmax_scaling KMeansSMOTE gbdt 0.05 100 0.1 0.1 1.0 0.8 10 5 0.9876103706946605 0.8685306532899453 0.8303217288431941 0.8092965157481593 0.9592309417836985 0.9936518407305781 0.9966115244112512 0.9985945651210407 0.9887583083957047 0.7434094658493124 0.6640319332751371 0.619998466375278 0.9297035751716924 100.2 5400.0 7.6 61.4
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106 lightgbm minmax_scaling KMeansSMOTE gbdt 0.1 100 0.1 0.1 0.8 0.8 10 5 0.9876463031277595 0.8696452935291772 0.832345622530247 0.8117216735235644 0.9573224840422384 0.9936693371232203 0.99655223796351 0.9984836046960821 0.988901862104868 0.7456212499351342 0.6681390070969839 0.6249597423510467 0.9257431059796088 101.0 5399.4 8.2 60.6
107 lightgbm minmax_scaling KMeansSMOTE gbdt 0.1 100 0.1 0.1 0.8 1.0 10 5 0.9879335756123682 0.8735142470990855 0.8373885067575266 0.81727407420208 0.9572776440521376 0.9938154842661611 0.9965889028545231 0.9984466224475614 0.9892278794141068 0.7532130099320097 0.6781881106605302 0.6361015259565985 0.9253274086901682 102.8 5399.2 8.4 58.8
108 lightgbm minmax_scaling KMeansSMOTE gbdt 0.1 100 0.1 0.1 1.0 0.8 10 5 0.9874667118857822 0.8680481539924493 0.8317553825774301 0.8116330485442184 0.9529694195282257 0.993576898041232 0.9964044843956639 0.9982986866137802 0.9889004742854578 0.7425194099436665 0.6671062807591965 0.6249674104746569 0.9170383647709939 101.0 5398.4 9.2 60.6
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110 lightgbm minmax_scaling KMeansSMOTE dart 0.03 100 0.1 0.1 0.8 0.8 10 5 0.9864612162803 0.8517213882755159 0.8087404085309796 0.785885510418824 0.9614784852134713 0.9930685455041811 0.9965093140147973 0.9988164928106563 0.9873866482719492 0.7103742310468509 0.620971503047162 0.5729545280269918 0.9355703221549934 92.6 5401.2 6.4 69.0
111 lightgbm minmax_scaling KMeansSMOTE dart 0.03 100 0.1 0.1 0.8 1.0 10 5 0.9862816572762855 0.8494531720877854 0.8062811698729515 0.7834055481958557 0.9607205770272069 0.9929769287184236 0.9964502905368763 0.9987795037224373 0.9872419067787573 0.7059294154571474 0.6161120492090266 0.5680315926692738 0.9341992472756562 91.8 5401.0 6.6 69.8
112 lightgbm minmax_scaling KMeansSMOTE dart 0.03 100 0.1 0.1 1.0 0.8 10 5 0.9868203342883293 0.8567123113396862 0.81462313212734 0.7920698521104123 0.9624609919498163 0.993251161582376 0.9965828464697711 0.9988164928106563 0.9877478834902378 0.7201734610969963 0.632663417784909 0.585323211410168 0.9371741004093945 94.6 5401.2 6.4 67.0
113 lightgbm minmax_scaling KMeansSMOTE dart 0.03 100 0.1 0.1 1.0 1.0 10 5 0.9863893900996572 0.8516614690849792 0.8095557057072785 0.7870576057488851 0.9579981706050438 0.9930309459196085 0.9964058116257544 0.9986685432974787 0.9874568744274971 0.7102919922503499 0.6227055997888027 0.5754466682002913 0.9285394667825905 93.0 5400.4 7.2 68.6
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121 lightgbm minmax_scaling KMeansSMOTE dart 0.1 100 0.1 0.1 1.0 1.0 10 5 0.98804128264537 0.8754763636546178 0.8405284491350876 0.8209184704711975 0.9551236805467838 0.9938696715557122 0.9965444000780066 0.9983356757019992 0.9894439864987652 0.7570830557535235 0.6845124981921686 0.6435012652403957 0.9208033745948025 104.0 5398.6 9.0 57.6
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123 lightgbm minmax_scaling class_weight gbdt 0.03 100 0.1 0.1 0.8 1.0 10 5 0.9873948857051393 0.8660654376430875 0.8278053873421346 0.8067827329934085 0.9571174874451384 0.9935416656406844 0.9965229981920896 0.9985206074636975 0.9886125885446072 0.7385892096454906 0.6590877764921795 0.6150448585231194 0.9256223863456696 99.4 5399.6 8.0 62.2
124 lightgbm minmax_scaling class_weight gbdt 0.03 100 0.1 0.1 1.0 0.8 10 5 0.9872871270913975 0.8636064344844797 0.8240482741313839 0.8025088748233425 0.9596889613355953 0.9934880088638021 0.9965897765183312 0.9986685432974788 0.9883616207181698 0.7337248601051574 0.6515067717444367 0.6063492063492064 0.9310163019530208 98.0 5400.4 7.2 63.6
125 lightgbm minmax_scaling class_weight gbdt 0.03 100 0.1 0.1 1.0 1.0 10 5 0.9871434811777045 0.863449771888091 0.8256341947711338 0.8048454076194018 0.9533828520533423 0.9934127773860718 0.9963827135574705 0.9983726647902182 0.9885024551041862 0.7334867663901103 0.6548856759847967 0.6113181504485852 0.9182632490024984 98.8 5398.8 8.8 62.8
126 lightgbm minmax_scaling class_weight gbdt 0.05 100 0.1 0.1 0.8 0.8 10 5 0.987358998405188 0.8658212328853709 0.8277045950376559 0.8067604078073429 0.9566247977036533 0.9935230811207543 0.9964934015324072 0.998483625215177 0.9886121814446716 0.7381193846499876 0.6589157885429044 0.6150371903995093 0.9246374139626351 99.4 5399.4 8.2 62.2
127 lightgbm minmax_scaling class_weight gbdt 0.05 100 0.1 0.1 0.8 1.0 10 5 0.9876462902325743 0.8702280820945317 0.8337605692544805 0.8135218826445911 0.9553624954113376 0.9936685848798849 0.9964855981899191 0.9983726511108216 0.9890093876458101 0.7467875793091783 0.6710355403190421 0.6286711141783605 0.9217156031768651 101.6 5398.8 8.8 60.0
128 lightgbm minmax_scaling class_weight gbdt 0.05 100 0.1 0.1 1.0 0.8 10 5 0.9876103706946605 0.8685306532899453 0.8303217288431941 0.8092965157481593 0.9592309417836985 0.9936518407305781 0.9966115244112512 0.9985945651210407 0.9887583083957047 0.7434094658493124 0.6640319332751371 0.619998466375278 0.9297035751716924 100.2 5400.0 7.6 61.4
129 lightgbm minmax_scaling class_weight gbdt 0.05 100 0.1 0.1 1.0 1.0 10 5 0.9875744640519315 0.8697383859570031 0.8335824387321773 0.8134887344578754 0.9538702717550708 0.993631447606049 0.9964264483135749 0.9982986866137802 0.9890083881850659 0.7458453243079577 0.6707384291507799 0.6286787823019708 0.9187321553250758 101.6 5398.4 9.2 60.0
130 lightgbm minmax_scaling class_weight gbdt 0.1 100 0.1 0.1 0.8 0.8 10 5 0.9876463031277595 0.8696452935291772 0.832345622530247 0.8117216735235644 0.9573224840422384 0.9936693371232203 0.99655223796351 0.9984836046960821 0.988901862104868 0.7456212499351342 0.6681390070969839 0.6249597423510467 0.9257431059796088 101.0 5399.4 8.2 60.6
131 lightgbm minmax_scaling class_weight gbdt 0.1 100 0.1 0.1 0.8 1.0 10 5 0.9879335756123682 0.8735142470990855 0.8373885067575266 0.81727407420208 0.9572776440521376 0.9938154842661611 0.9965889028545231 0.9984466224475614 0.9892278794141068 0.7532130099320097 0.6781881106605302 0.6361015259565985 0.9253274086901682 102.8 5399.2 8.4 58.8
132 lightgbm minmax_scaling class_weight gbdt 0.1 100 0.1 0.1 1.0 0.8 10 5 0.9874667118857822 0.8680481539924493 0.8317553825774301 0.8116330485442184 0.9529694195282257 0.993576898041232 0.9964044843956639 0.9982986866137802 0.9889004742854578 0.7425194099436665 0.6671062807591965 0.6249674104746569 0.9170383647709939 101.0 5398.4 9.2 60.6
133 lightgbm minmax_scaling class_weight gbdt 0.1 100 0.1 0.1 1.0 1.0 10 5 0.9879694887026897 0.8749776173022346 0.840353596310616 0.8209121572972682 0.9537927663883565 0.9938325369626163 0.9964852212462812 0.9982617043652595 0.9894432107974284 0.7561226976418527 0.6842219713749508 0.6435626102292769 0.9181423219792844 104.0 5398.2 9.4 57.6
134 lightgbm minmax_scaling class_weight dart 0.03 100 0.1 0.1 0.8 0.8 10 5 0.9864612162803 0.8517213882755159 0.8087404085309796 0.785885510418824 0.9614784852134713 0.9930685455041811 0.9965093140147973 0.9988164928106563 0.9873866482719492 0.7103742310468509 0.620971503047162 0.5729545280269918 0.9355703221549934 92.6 5401.2 6.4 69.0
135 lightgbm minmax_scaling class_weight dart 0.03 100 0.1 0.1 0.8 1.0 10 5 0.9862816572762855 0.8494531720877854 0.8062811698729515 0.7834055481958557 0.9607205770272069 0.9929769287184236 0.9964502905368763 0.9987795037224373 0.9872419067787573 0.7059294154571474 0.6161120492090266 0.5680315926692738 0.9341992472756562 91.8 5401.0 6.6 69.8
136 lightgbm minmax_scaling class_weight dart 0.03 100 0.1 0.1 1.0 0.8 10 5 0.9868203342883293 0.8567123113396862 0.81462313212734 0.7920698521104123 0.9624609919498163 0.993251161582376 0.9965828464697711 0.9988164928106563 0.9877478834902378 0.7201734610969963 0.632663417784909 0.585323211410168 0.9371741004093945 94.6 5401.2 6.4 67.0
137 lightgbm minmax_scaling class_weight dart 0.03 100 0.1 0.1 1.0 1.0 10 5 0.9863893900996572 0.8516614690849792 0.8095557057072785 0.7870576057488851 0.9579981706050438 0.9930309459196085 0.9964058116257544 0.9986685432974787 0.9874568744274971 0.7102919922503499 0.6227055997888027 0.5754466682002913 0.9285394667825905 93.0 5400.4 7.2 68.6
138 lightgbm minmax_scaling class_weight dart 0.05 100 0.1 0.1 0.8 0.8 10 5 0.9872871464341749 0.8647825564498515 0.8265211036628628 0.8055143377206931 0.9560208920519685 0.9934866215661462 0.9964787449584944 0.998483625215177 0.9885398629294059 0.7360784913335567 0.6565634623672312 0.6125450502262096 0.9235019211745309 99.0 5399.4 8.2 62.6
139 lightgbm minmax_scaling class_weight dart 0.05 100 0.1 0.1 0.8 1.0 10 5 0.9872512526866313 0.864242117798965 0.8255296821802522 0.8043135971909393 0.9568082528265623 0.9934684617996755 0.9964935591422892 0.9985206074636975 0.9884674795460324 0.7350157737982544 0.6545658052182155 0.6101065869181811 0.9251490261070924 98.6 5399.6 8.0 63.0
140 lightgbm minmax_scaling class_weight dart 0.05 100 0.1 0.1 1.0 0.8 10 5 0.9875385574092025 0.867739957700573 0.8292362302142348 0.808058120624818 0.9590571825576781 0.9936151348160316 0.9965968036054408 0.9985945788004372 0.9886852713014047 0.7418647805851146 0.6618756568230287 0.6175216624491988 0.9294290938139514 99.8 5400.0 7.6 61.8
141 lightgbm minmax_scaling class_weight dart 0.05 100 0.1 0.1 1.0 1.0 10 5 0.9878617429841328 0.8718918805916948 0.8343953837040834 0.8136405111931599 0.9597257588431093 0.9937798152798208 0.9966630340635951 0.998594571960739 0.9890116522948971 0.7500039459035688 0.6721277333445717 0.6286864504255809 0.9304398653913216 101.6 5400.0 7.6 60.0
142 lightgbm minmax_scaling class_weight dart 0.1 100 0.1 0.1 0.8 0.8 10 5 0.9875744511567464 0.8694904253032434 0.8330898747073772 0.8128784428659526 0.9543165483350643 0.9936317630222906 0.9964486981215537 0.9983356757019992 0.9889726061606664 0.7453490875841962 0.6697310512932007 0.6274212100299057 0.9196604905094622 101.4 5398.6 9.0 60.2
143 lightgbm minmax_scaling class_weight dart 0.1 100 0.1 0.1 0.8 1.0 10 5 0.9877540230559461 0.8705274306318904 0.8326760658152557 0.8117771571558929 0.9596652466682235 0.993724889952756 0.9966409360538494 0.998594571960739 0.9889028548898798 0.7473299713110246 0.6687111955766621 0.6249597423510467 0.930427638446567 101.0 5400.0 7.6 60.6
144 lightgbm minmax_scaling class_weight dart 0.1 100 0.1 0.1 1.0 0.8 10 5 0.9878976496268617 0.8740424916234224 0.8391944377539111 0.819654574765656 0.9536373604366759 0.9937959382660164 0.9964704965030846 0.9982616838461646 0.9893706349659366 0.7542890449808285 0.681918379004738 0.6410474656851469 0.9179040859074152 103.6 5398.2 9.4 58.0
145 lightgbm minmax_scaling class_weight dart 0.1 100 0.1 0.1 1.0 1.0 10 5 0.98804128264537 0.8754763636546178 0.8405284491350876 0.8209184704711975 0.9551236805467838 0.9938696715557122 0.9965444000780066 0.9983356757019992 0.9894439864987652 0.7570830557535235 0.6845124981921686 0.6435012652403957 0.9208033745948025 104.0 5398.6 9.0 57.6
146 lightgbm yeo_johnson KMeansSMOTE gbdt 0.03 100 0.1 0.1 0.8 0.8 10 5 0.9872871528817676 0.8635827226233982 0.82363889290808 0.8019139194786398 0.9607516165514827 0.9934880771322151 0.9966119285054977 0.9987055323856977 0.9883253364382266 0.7336773681145814 0.6506658573106621 0.605122306571582 0.9331778966647388 97.8 5400.6 7.0 63.8
147 lightgbm yeo_johnson KMeansSMOTE gbdt 0.03 100 0.1 0.1 0.8 1.0 10 5 0.9873948857051393 0.8660654376430875 0.8278053873421346 0.8067827329934085 0.9571174874451384 0.9935416656406844 0.9965229981920896 0.9985206074636975 0.9886125885446072 0.7385892096454906 0.6590877764921795 0.6150448585231194 0.9256223863456696 99.4 5399.6 8.0 62.2
148 lightgbm yeo_johnson KMeansSMOTE gbdt 0.03 100 0.1 0.1 1.0 0.8 10 5 0.9872871270913975 0.8636064344844797 0.8240482741313839 0.8025088748233425 0.9596889613355953 0.9934880088638021 0.9965897765183312 0.9986685432974788 0.9883616207181698 0.7337248601051574 0.6515067717444367 0.6063492063492064 0.9310163019530208 98.0 5400.4 7.2 63.6
149 lightgbm yeo_johnson KMeansSMOTE gbdt 0.03 100 0.1 0.1 1.0 1.0 10 5 0.9871434811777045 0.863449771888091 0.8256341947711338 0.8048454076194018 0.9533828520533423 0.9934127773860718 0.9963827135574705 0.9983726647902182 0.9885024551041862 0.7334867663901103 0.6548856759847967 0.6113181504485852 0.9182632490024984 98.8 5398.8 8.8 62.8
150 lightgbm yeo_johnson KMeansSMOTE gbdt 0.05 100 0.1 0.1 0.8 0.8 10 5 0.987358998405188 0.8658212328853709 0.8277045950376559 0.8067604078073429 0.9566247977036533 0.9935230811207543 0.9964934015324072 0.998483625215177 0.9886121814446716 0.7381193846499876 0.6589157885429044 0.6150371903995093 0.9246374139626351 99.4 5399.4 8.2 62.2
151 lightgbm yeo_johnson KMeansSMOTE gbdt 0.05 100 0.1 0.1 0.8 1.0 10 5 0.9876462902325743 0.8702280820945317 0.8337605692544805 0.8135218826445911 0.9553624954113376 0.9936685848798849 0.9964855981899191 0.9983726511108216 0.9890093876458101 0.7467875793091783 0.6710355403190421 0.6286711141783605 0.9217156031768651 101.6 5398.8 8.8 60.0
152 lightgbm yeo_johnson KMeansSMOTE gbdt 0.05 100 0.1 0.1 1.0 0.8 10 5 0.9876103706946605 0.8685306532899453 0.8303217288431941 0.8092965157481593 0.9592309417836985 0.9936518407305781 0.9966115244112512 0.9985945651210407 0.9887583083957047 0.7434094658493124 0.6640319332751371 0.619998466375278 0.9297035751716924 100.2 5400.0 7.6 61.4
153 lightgbm yeo_johnson KMeansSMOTE gbdt 0.05 100 0.1 0.1 1.0 1.0 10 5 0.9875744640519315 0.8697383859570031 0.8335824387321773 0.8134887344578754 0.9538702717550708 0.993631447606049 0.9964264483135749 0.9982986866137802 0.9890083881850659 0.7458453243079577 0.6707384291507799 0.6286787823019708 0.9187321553250758 101.6 5398.4 9.2 60.0
154 lightgbm yeo_johnson KMeansSMOTE gbdt 0.1 100 0.1 0.1 0.8 0.8 10 5 0.9876463031277595 0.8696452935291772 0.832345622530247 0.8117216735235644 0.9573224840422384 0.9936693371232203 0.99655223796351 0.9984836046960821 0.988901862104868 0.7456212499351342 0.6681390070969839 0.6249597423510467 0.9257431059796088 101.0 5399.4 8.2 60.6
155 lightgbm yeo_johnson KMeansSMOTE gbdt 0.1 100 0.1 0.1 0.8 1.0 10 5 0.9879335756123682 0.8735142470990855 0.8373885067575266 0.81727407420208 0.9572776440521376 0.9938154842661611 0.9965889028545231 0.9984466224475614 0.9892278794141068 0.7532130099320097 0.6781881106605302 0.6361015259565985 0.9253274086901682 102.8 5399.2 8.4 58.8
156 lightgbm yeo_johnson KMeansSMOTE gbdt 0.1 100 0.1 0.1 1.0 0.8 10 5 0.9874667118857822 0.8680481539924493 0.8317553825774301 0.8116330485442184 0.9529694195282257 0.993576898041232 0.9964044843956639 0.9982986866137802 0.9889004742854578 0.7425194099436665 0.6671062807591965 0.6249674104746569 0.9170383647709939 101.0 5398.4 9.2 60.6
157 lightgbm yeo_johnson KMeansSMOTE gbdt 0.1 100 0.1 0.1 1.0 1.0 10 5 0.9879694887026897 0.8749776173022346 0.840353596310616 0.8209121572972682 0.9537927663883565 0.9938325369626163 0.9964852212462812 0.9982617043652595 0.9894432107974284 0.7561226976418527 0.6842219713749508 0.6435626102292769 0.9181423219792844 104.0 5398.2 9.4 57.6
158 lightgbm yeo_johnson KMeansSMOTE dart 0.03 100 0.1 0.1 0.8 0.8 10 5 0.9864612162803 0.8517213882755159 0.8087404085309796 0.785885510418824 0.9614784852134713 0.9930685455041811 0.9965093140147973 0.9988164928106563 0.9873866482719492 0.7103742310468509 0.620971503047162 0.5729545280269918 0.9355703221549934 92.6 5401.2 6.4 69.0
159 lightgbm yeo_johnson KMeansSMOTE dart 0.03 100 0.1 0.1 0.8 1.0 10 5 0.9862816572762855 0.8494531720877854 0.8062811698729515 0.7834055481958557 0.9607205770272069 0.9929769287184236 0.9964502905368763 0.9987795037224373 0.9872419067787573 0.7059294154571474 0.6161120492090266 0.5680315926692738 0.9341992472756562 91.8 5401.0 6.6 69.8
160 lightgbm yeo_johnson KMeansSMOTE dart 0.03 100 0.1 0.1 1.0 0.8 10 5 0.9868203342883293 0.8567123113396862 0.81462313212734 0.7920698521104123 0.9624609919498163 0.993251161582376 0.9965828464697711 0.9988164928106563 0.9877478834902378 0.7201734610969963 0.632663417784909 0.585323211410168 0.9371741004093945 94.6 5401.2 6.4 67.0
161 lightgbm yeo_johnson KMeansSMOTE dart 0.03 100 0.1 0.1 1.0 1.0 10 5 0.9863893900996572 0.8516614690849792 0.8095557057072785 0.7870576057488851 0.9579981706050438 0.9930309459196085 0.9964058116257544 0.9986685432974787 0.9874568744274971 0.7102919922503499 0.6227055997888027 0.5754466682002913 0.9285394667825905 93.0 5400.4 7.2 68.6
162 lightgbm yeo_johnson KMeansSMOTE dart 0.05 100 0.1 0.1 0.8 0.8 10 5 0.9872871464341749 0.8647825564498515 0.8265211036628628 0.8055143377206931 0.9560208920519685 0.9934866215661462 0.9964787449584944 0.998483625215177 0.9885398629294059 0.7360784913335567 0.6565634623672312 0.6125450502262096 0.9235019211745309 99.0 5399.4 8.2 62.6
163 lightgbm yeo_johnson KMeansSMOTE dart 0.05 100 0.1 0.1 0.8 1.0 10 5 0.9872512526866313 0.864242117798965 0.8255296821802522 0.8043135971909393 0.9568082528265623 0.9934684617996755 0.9964935591422892 0.9985206074636975 0.9884674795460324 0.7350157737982544 0.6545658052182155 0.6101065869181811 0.9251490261070924 98.6 5399.6 8.0 63.0
164 lightgbm yeo_johnson KMeansSMOTE dart 0.05 100 0.1 0.1 1.0 0.8 10 5 0.9875385574092025 0.867739957700573 0.8292362302142348 0.808058120624818 0.9590571825576781 0.9936151348160316 0.9965968036054408 0.9985945788004372 0.9886852713014047 0.7418647805851146 0.6618756568230287 0.6175216624491988 0.9294290938139514 99.8 5400.0 7.6 61.8
165 lightgbm yeo_johnson KMeansSMOTE dart 0.05 100 0.1 0.1 1.0 1.0 10 5 0.9878617429841328 0.8718918805916948 0.8343953837040834 0.8136405111931599 0.9597257588431093 0.9937798152798208 0.9966630340635951 0.998594571960739 0.9890116522948971 0.7500039459035688 0.6721277333445717 0.6286864504255809 0.9304398653913216 101.6 5400.0 7.6 60.0
166 lightgbm yeo_johnson KMeansSMOTE dart 0.1 100 0.1 0.1 0.8 0.8 10 5 0.9875744511567464 0.8694904253032434 0.8330898747073772 0.8128784428659526 0.9543165483350643 0.9936317630222906 0.9964486981215537 0.9983356757019992 0.9889726061606664 0.7453490875841962 0.6697310512932007 0.6274212100299057 0.9196604905094622 101.4 5398.6 9.0 60.2
167 lightgbm yeo_johnson KMeansSMOTE dart 0.1 100 0.1 0.1 0.8 1.0 10 5 0.9877540230559461 0.8705274306318904 0.8326760658152557 0.8117771571558929 0.9596652466682235 0.993724889952756 0.9966409360538494 0.998594571960739 0.9889028548898798 0.7473299713110246 0.6687111955766621 0.6249597423510467 0.930427638446567 101.0 5400.0 7.6 60.6
168 lightgbm yeo_johnson KMeansSMOTE dart 0.1 100 0.1 0.1 1.0 0.8 10 5 0.9878976496268617 0.8740424916234224 0.8391944377539111 0.819654574765656 0.9536373604366759 0.9937959382660164 0.9964704965030846 0.9982616838461646 0.9893706349659366 0.7542890449808285 0.681918379004738 0.6410474656851469 0.9179040859074152 103.6 5398.2 9.4 58.0
169 lightgbm yeo_johnson KMeansSMOTE dart 0.1 100 0.1 0.1 1.0 1.0 10 5 0.98804128264537 0.8754763636546178 0.8405284491350876 0.8209184704711975 0.9551236805467838 0.9938696715557122 0.9965444000780066 0.9983356757019992 0.9894439864987652 0.7570830557535235 0.6845124981921686 0.6435012652403957 0.9208033745948025 104.0 5398.6 9.0 57.6
170 lightgbm yeo_johnson class_weight gbdt 0.03 100 0.1 0.1 0.8 0.8 10 5 0.9872871528817676 0.8635827226233982 0.82363889290808 0.8019139194786398 0.9607516165514827 0.9934880771322151 0.9966119285054977 0.9987055323856977 0.9883253364382266 0.7336773681145814 0.6506658573106621 0.605122306571582 0.9331778966647388 97.8 5400.6 7.0 63.8
171 lightgbm yeo_johnson class_weight gbdt 0.03 100 0.1 0.1 0.8 1.0 10 5 0.9873948857051393 0.8660654376430875 0.8278053873421346 0.8067827329934085 0.9571174874451384 0.9935416656406844 0.9965229981920896 0.9985206074636975 0.9886125885446072 0.7385892096454906 0.6590877764921795 0.6150448585231194 0.9256223863456696 99.4 5399.6 8.0 62.2
172 lightgbm yeo_johnson class_weight gbdt 0.03 100 0.1 0.1 1.0 0.8 10 5 0.9872871270913975 0.8636064344844797 0.8240482741313839 0.8025088748233425 0.9596889613355953 0.9934880088638021 0.9965897765183312 0.9986685432974788 0.9883616207181698 0.7337248601051574 0.6515067717444367 0.6063492063492064 0.9310163019530208 98.0 5400.4 7.2 63.6
173 lightgbm yeo_johnson class_weight gbdt 0.03 100 0.1 0.1 1.0 1.0 10 5 0.9871434811777045 0.863449771888091 0.8256341947711338 0.8048454076194018 0.9533828520533423 0.9934127773860718 0.9963827135574705 0.9983726647902182 0.9885024551041862 0.7334867663901103 0.6548856759847967 0.6113181504485852 0.9182632490024984 98.8 5398.8 8.8 62.8
174 lightgbm yeo_johnson class_weight gbdt 0.05 100 0.1 0.1 0.8 0.8 10 5 0.987358998405188 0.8658212328853709 0.8277045950376559 0.8067604078073429 0.9566247977036533 0.9935230811207543 0.9964934015324072 0.998483625215177 0.9886121814446716 0.7381193846499876 0.6589157885429044 0.6150371903995093 0.9246374139626351 99.4 5399.4 8.2 62.2
175 lightgbm yeo_johnson class_weight gbdt 0.05 100 0.1 0.1 0.8 1.0 10 5 0.9876462902325743 0.8702280820945317 0.8337605692544805 0.8135218826445911 0.9553624954113376 0.9936685848798849 0.9964855981899191 0.9983726511108216 0.9890093876458101 0.7467875793091783 0.6710355403190421 0.6286711141783605 0.9217156031768651 101.6 5398.8 8.8 60.0
176 lightgbm yeo_johnson class_weight gbdt 0.05 100 0.1 0.1 1.0 0.8 10 5 0.9876103706946605 0.8685306532899453 0.8303217288431941 0.8092965157481593 0.9592309417836985 0.9936518407305781 0.9966115244112512 0.9985945651210407 0.9887583083957047 0.7434094658493124 0.6640319332751371 0.619998466375278 0.9297035751716924 100.2 5400.0 7.6 61.4
177 lightgbm yeo_johnson class_weight gbdt 0.05 100 0.1 0.1 1.0 1.0 10 5 0.9875744640519315 0.8697383859570031 0.8335824387321773 0.8134887344578754 0.9538702717550708 0.993631447606049 0.9964264483135749 0.9982986866137802 0.9890083881850659 0.7458453243079577 0.6707384291507799 0.6286787823019708 0.9187321553250758 101.6 5398.4 9.2 60.0
178 lightgbm yeo_johnson class_weight gbdt 0.1 100 0.1 0.1 0.8 0.8 10 5 0.9876463031277595 0.8696452935291772 0.832345622530247 0.8117216735235644 0.9573224840422384 0.9936693371232203 0.99655223796351 0.9984836046960821 0.988901862104868 0.7456212499351342 0.6681390070969839 0.6249597423510467 0.9257431059796088 101.0 5399.4 8.2 60.6
179 lightgbm yeo_johnson class_weight gbdt 0.1 100 0.1 0.1 0.8 1.0 10 5 0.9879335756123682 0.8735142470990855 0.8373885067575266 0.81727407420208 0.9572776440521376 0.9938154842661611 0.9965889028545231 0.9984466224475614 0.9892278794141068 0.7532130099320097 0.6781881106605302 0.6361015259565985 0.9253274086901682 102.8 5399.2 8.4 58.8
180 lightgbm yeo_johnson class_weight gbdt 0.1 100 0.1 0.1 1.0 0.8 10 5 0.9874667118857822 0.8680481539924493 0.8317553825774301 0.8116330485442184 0.9529694195282257 0.993576898041232 0.9964044843956639 0.9982986866137802 0.9889004742854578 0.7425194099436665 0.6671062807591965 0.6249674104746569 0.9170383647709939 101.0 5398.4 9.2 60.6
181 lightgbm yeo_johnson class_weight gbdt 0.1 100 0.1 0.1 1.0 1.0 10 5 0.9879694887026897 0.8749776173022346 0.840353596310616 0.8209121572972682 0.9537927663883565 0.9938325369626163 0.9964852212462812 0.9982617043652595 0.9894432107974284 0.7561226976418527 0.6842219713749508 0.6435626102292769 0.9181423219792844 104.0 5398.2 9.4 57.6
182 lightgbm yeo_johnson class_weight dart 0.03 100 0.1 0.1 0.8 0.8 10 5 0.9864612162803 0.8517213882755159 0.8087404085309796 0.785885510418824 0.9614784852134713 0.9930685455041811 0.9965093140147973 0.9988164928106563 0.9873866482719492 0.7103742310468509 0.620971503047162 0.5729545280269918 0.9355703221549934 92.6 5401.2 6.4 69.0
183 lightgbm yeo_johnson class_weight dart 0.03 100 0.1 0.1 0.8 1.0 10 5 0.9862816572762855 0.8494531720877854 0.8062811698729515 0.7834055481958557 0.9607205770272069 0.9929769287184236 0.9964502905368763 0.9987795037224373 0.9872419067787573 0.7059294154571474 0.6161120492090266 0.5680315926692738 0.9341992472756562 91.8 5401.0 6.6 69.8
184 lightgbm yeo_johnson class_weight dart 0.03 100 0.1 0.1 1.0 0.8 10 5 0.9868203342883293 0.8567123113396862 0.81462313212734 0.7920698521104123 0.9624609919498163 0.993251161582376 0.9965828464697711 0.9988164928106563 0.9877478834902378 0.7201734610969963 0.632663417784909 0.585323211410168 0.9371741004093945 94.6 5401.2 6.4 67.0
185 lightgbm yeo_johnson class_weight dart 0.03 100 0.1 0.1 1.0 1.0 10 5 0.9863893900996572 0.8516614690849792 0.8095557057072785 0.7870576057488851 0.9579981706050438 0.9930309459196085 0.9964058116257544 0.9986685432974787 0.9874568744274971 0.7102919922503499 0.6227055997888027 0.5754466682002913 0.9285394667825905 93.0 5400.4 7.2 68.6
186 lightgbm yeo_johnson class_weight dart 0.05 100 0.1 0.1 0.8 0.8 10 5 0.9872871464341749 0.8647825564498515 0.8265211036628628 0.8055143377206931 0.9560208920519685 0.9934866215661462 0.9964787449584944 0.998483625215177 0.9885398629294059 0.7360784913335567 0.6565634623672312 0.6125450502262096 0.9235019211745309 99.0 5399.4 8.2 62.6
187 lightgbm yeo_johnson class_weight dart 0.05 100 0.1 0.1 0.8 1.0 10 5 0.9872512526866313 0.864242117798965 0.8255296821802522 0.8043135971909393 0.9568082528265623 0.9934684617996755 0.9964935591422892 0.9985206074636975 0.9884674795460324 0.7350157737982544 0.6545658052182155 0.6101065869181811 0.9251490261070924 98.6 5399.6 8.0 63.0
188 lightgbm yeo_johnson class_weight dart 0.05 100 0.1 0.1 1.0 0.8 10 5 0.9875385574092025 0.867739957700573 0.8292362302142348 0.808058120624818 0.9590571825576781 0.9936151348160316 0.9965968036054408 0.9985945788004372 0.9886852713014047 0.7418647805851146 0.6618756568230287 0.6175216624491988 0.9294290938139514 99.8 5400.0 7.6 61.8
189 lightgbm yeo_johnson class_weight dart 0.05 100 0.1 0.1 1.0 1.0 10 5 0.9878617429841328 0.8718918805916948 0.8343953837040834 0.8136405111931599 0.9597257588431093 0.9937798152798208 0.9966630340635951 0.998594571960739 0.9890116522948971 0.7500039459035688 0.6721277333445717 0.6286864504255809 0.9304398653913216 101.6 5400.0 7.6 60.0
190 lightgbm yeo_johnson class_weight dart 0.1 100 0.1 0.1 0.8 0.8 10 5 0.9875744511567464 0.8694904253032434 0.8330898747073772 0.8128784428659526 0.9543165483350643 0.9936317630222906 0.9964486981215537 0.9983356757019992 0.9889726061606664 0.7453490875841962 0.6697310512932007 0.6274212100299057 0.9196604905094622 101.4 5398.6 9.0 60.2
191 lightgbm yeo_johnson class_weight dart 0.1 100 0.1 0.1 0.8 1.0 10 5 0.9877540230559461 0.8705274306318904 0.8326760658152557 0.8117771571558929 0.9596652466682235 0.993724889952756 0.9966409360538494 0.998594571960739 0.9889028548898798 0.7473299713110246 0.6687111955766621 0.6249597423510467 0.930427638446567 101.0 5400.0 7.6 60.6
192 lightgbm yeo_johnson class_weight dart 0.1 100 0.1 0.1 1.0 0.8 10 5 0.9878976496268617 0.8740424916234224 0.8391944377539111 0.819654574765656 0.9536373604366759 0.9937959382660164 0.9964704965030846 0.9982616838461646 0.9893706349659366 0.7542890449808285 0.681918379004738 0.6410474656851469 0.9179040859074152 103.6 5398.2 9.4 58.0
193 lightgbm yeo_johnson class_weight dart 0.1 100 0.1 0.1 1.0 1.0 10 5 0.98804128264537 0.8754763636546178 0.8405284491350876 0.8209184704711975 0.9551236805467838 0.9938696715557122 0.9965444000780066 0.9983356757019992 0.9894439864987652 0.7570830557535235 0.6845124981921686 0.6435012652403957 0.9208033745948025 104.0 5398.6 9.0 57.6

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from itertools import product
from operator import sub
from tabnanny import verbose
import pandas
from catboost import CatBoostClassifier
from imblearn.over_sampling import KMeansSMOTE
from model_utils import average_fold_results, get_metrics, scaling_handler
from sklearn.model_selection import StratifiedKFold, train_test_split
from tqdm import tqdm
class CAT_BOOST:
def __init__(self, data_frame, params={}, n_split_kfold=5, test_size=0.15, seed=42):
self.data_frame = data_frame
self.params = params
self.n_split_kfold = n_split_kfold
self.test_size = test_size
self.seed = 42
self.x_test = None
self.y_test = None
self.scaling_method = self.params.get("scaling_method", None)
self.sampling_method = self.params.get("sampling_method", None)
self.class_weights = {0: 1.0, 1: 1.0}
self.model = None
self.iterations = self.params.get("iterations", 100)
self.learning_rate = self.params.get("learning_rate", 0.1)
self.depth = self.params.get("depth", 6)
self.l2_leaf_reg = self.params.get("l2_leaf_reg", 3)
self.subsample = self.params.get("subsample", 0.6)
self.k_neighbors = self.params.get("k_neighbors", 10)
self.kmeans_estimator = self.params.get("kmeans_estimator", 5)
self.tuning_results = None
def preprocess(self):
self.scaling_method = self.params.get("scaling_method", None)
if self.scaling_method:
self.data_frame = scaling_handler(self.data_frame, self.scaling_method)
def fit(self):
y = self.data_frame["label"]
X = self.data_frame.drop(columns=["label"])
x_train_val, self.x_test, y_train_val, self.y_test = train_test_split(
X, y, test_size=self.test_size, stratify=y, random_state=self.seed
)
skf = StratifiedKFold(
n_splits=self.n_split_kfold, shuffle=True, random_state=self.seed
)
fold_results = []
for fold_idx, (train_index, val_index) in enumerate(
tqdm(
skf.split(x_train_val, y_train_val),
total=self.n_split_kfold,
desc=" >> CatBoost Fitting: ",
)
):
x_train_fold, x_val = (
x_train_val.iloc[train_index],
x_train_val.iloc[val_index],
)
y_train_fold, y_val = (
y_train_val.iloc[train_index],
y_train_val.iloc[val_index],
)
self.sampling_method = self.params.get("sampling_method", None)
if self.sampling_method == "KMeansSMOTE":
smote = KMeansSMOTE(
sampling_strategy="minority",
k_neighbors=self.k_neighbors,
kmeans_estimator=self.kmeans_estimator,
cluster_balance_threshold=0.001,
random_state=self.seed,
n_jobs=-1,
)
x_train_fold, y_train_fold = smote.fit_resample(
x_train_fold, y_train_fold
)
y_train_fold = y_train_fold.astype(int)
elif self.sampling_method == "class_weight":
self.class_1_weight = int(
(y_train_fold.shape[0] - y_train_fold.sum()) / y_train_fold.sum()
)
self.class_weights = {0: 1, 1: self.class_1_weight}
self.model = CatBoostClassifier(
iterations=self.iterations,
learning_rate=self.learning_rate,
depth=self.depth,
l2_leaf_reg=self.l2_leaf_reg,
subsample=self.subsample,
verbose=False,
random_seed=self.seed,
class_weights=self.class_weights,
)
self.model.fit(x_train_fold, y_train_fold)
y_pred_val = self.model.predict(x_val)
val_metrics = get_metrics(y_val, y_pred_val)
fold_results.append(val_metrics)
return average_fold_results(fold_results)
def eval(self, x_test=None, y_test=None):
if x_test is not None and y_test is not None:
self.x_test = x_test
self.y_test = y_test
self.y_pred_test = self.model.predict(self.x_test)
test_metrics = get_metrics(self.y_test, self.y_pred_test)
return test_metrics
def tune(self):
scaling_methods = [
"standard_scaling",
"robust_scaling",
"minmax_scaling",
"yeo_johnson",
]
sampling_methods = [
"KMeansSMOTE",
"class_weight",
]
learning_rate_list = [0.03, 0.05, 0.1]
depth_list = [6, 8]
l2_leaf_reg_list = [1, 3]
subsample_list = [0.8, 1.0]
k_neighbors_list = [10]
kmeans_estimator_list = [5]
tuning_results = []
param_product = list(
product(
scaling_methods,
sampling_methods,
learning_rate_list,
depth_list,
l2_leaf_reg_list,
subsample_list,
k_neighbors_list,
kmeans_estimator_list,
)
)
for (
scaling_method,
sampling_method,
learning_rate,
depth,
l2_leaf_reg,
subsample,
k_neighbors,
kmeans_estimator,
) in tqdm(param_product, total=len(param_product), desc=" > CatBoost Tuning: "):
self.scaling_method = scaling_method
self.sampling_method = sampling_method
self.learning_rate = learning_rate
self.depth = depth
self.l2_leaf_reg = l2_leaf_reg
self.subsample = subsample
self.k_neighbors = k_neighbors
self.kmeans_estimator = kmeans_estimator
print(
" >> Fitting Params: ",
scaling_method,
sampling_method,
learning_rate,
depth,
l2_leaf_reg,
subsample,
k_neighbors,
kmeans_estimator,
)
self.preprocess()
fold_result = self.fit()
tuning_results.append(
{
"model": "cat_boost",
"scaling_method": scaling_method,
"sampling_method": sampling_method,
"learning_rate": learning_rate,
"depth": depth,
"l2_leaf_reg": l2_leaf_reg,
"subsample": subsample,
"k_neighbors": k_neighbors,
"kmeans_estimator": kmeans_estimator,
"metrics": fold_result,
}
)
self.tuning_results = tuning_results
# Save tuning results to CSV
df_tuning = pandas.DataFrame(tuning_results)
metrics_df = df_tuning["metrics"].apply(pandas.Series)
df_tuning = pandas.concat(
[df_tuning.drop(columns=["metrics"]), metrics_df], axis=1
)
df_tuning.to_csv("cat_boost_tuning_results.csv", index=False)
return

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import pandas
from catboost_model import CAT_BOOST
from lightgbm_model import LIGHT_GBM
data_frame = pandas.read_csv("./data/Ketamine_icp_no_missing.csv")
cat_boost_results = pandas.read_csv("./cat_boost_tuning_results.csv")
lgbm_results = pandas.read_csv("./lightgbm_tuning_results.csv")
def get_best_params(data_frame, metrics=["f2_class1", "f1_class1"]):
max_f2 = cat_boost_results[metrics[0]].max()
best_f2_rows = cat_boost_results[cat_boost_results[metrics[0]] == max_f2]
best_row = best_f2_rows.loc[best_f2_rows[metrics[1]].idxmax()]
return best_row.to_dict()
cat_boost_best_params = get_best_params(cat_boost_results)
cat_boost_model = CAT_BOOST(data_frame, params=cat_boost_best_params)
cat_boost_model.fit()
cat_test_metrics = cat_boost_model.eval()
print(cat_test_metrics)
x_test, y_test = cat_boost_model.x_test, cat_boost_model.y_test
lgbm_best_params = get_best_params(lgbm_results)
lgbm_model = LIGHT_GBM(data_frame, params=lgbm_best_params)
lgbm_model.fit()
lgbm_test_metrics = lgbm_model.eval(x_test, y_test)
print(lgbm_test_metrics)
import pandas as pd
def clean_metrics(metrics):
return {k: float(v) if hasattr(v, "item") else v for k, v in metrics.items()}
cat_test_metrics_clean = clean_metrics(cat_test_metrics)
lgbm_test_metrics_clean = clean_metrics(lgbm_test_metrics)
comparison_df = pd.DataFrame(
[
{"model": "catboost", **cat_test_metrics_clean},
{"model": "lightgbm", **lgbm_test_metrics_clean},
]
)
comparison_filename = "comparison_catboost_lightgbm.csv"
comparison_df.to_csv(comparison_filename, index=False)
print(f"Comparison saved to: {comparison_filename}")

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from itertools import product
from operator import sub
from tabnanny import verbose
import lightgbm as lgb
import pandas
from imblearn.over_sampling import KMeansSMOTE
from model_utils import average_fold_results, get_metrics, scaling_handler
from sklearn.model_selection import StratifiedKFold, train_test_split
from tqdm import tqdm
class LIGHT_GBM:
def __init__(self, data_frame, params={}, n_split_kfold=5, test_size=0.15, seed=42):
self.data_frame = data_frame
self.params = params
self.n_split_kfold = n_split_kfold
self.test_size = test_size
self.seed = 42
self.x_test = None
self.y_test = None
self.scaling_method = None
self.sampling_method = None
self.class_weights = {0: 1.0, 1: 1.0}
self.model = None
self.learning_rate = self.params.get("learning_rate", 0.1)
self.num_leaves = self.params.get("num_leaves", 100)
self.boosting_type = self.params.get("boosting_type", "gbdt")
self.l1_reg = self.params.get("l1_reg", 0.1)
self.l2_reg = self.params.get("l2_reg", 0.1)
self.subsample = self.params.get("subsample", 1.0)
self.tree_subsample = self.params.get("tree_subsample", 1.0)
self.k_neighbors = self.params.get("k_neighbors", 10)
self.kmeans_estimator = self.params.get("kmeans_estimator", 5)
self.tuning_results = None
def preprocess(self):
self.scaling_method = self.params.get("scaling_method", None)
if self.scaling_method:
self.data_frame = scaling_handler(self.data_frame, self.scaling_method)
def fit(self):
y = self.data_frame["label"]
X = self.data_frame.drop(columns=["label"])
x_train_val, self.x_test, y_train_val, self.y_test = train_test_split(
X, y, test_size=self.test_size, stratify=y, random_state=self.seed
)
skf = StratifiedKFold(
n_splits=self.n_split_kfold, shuffle=True, random_state=self.seed
)
fold_results = []
for fold_idx, (train_index, val_index) in enumerate(
tqdm(
skf.split(x_train_val, y_train_val),
total=self.n_split_kfold,
desc=" >> LightGBM Fitting: ",
)
):
x_train_fold, x_val = (
x_train_val.iloc[train_index],
x_train_val.iloc[val_index],
)
y_train_fold, y_val = (
y_train_val.iloc[train_index],
y_train_val.iloc[val_index],
)
self.sampling_method = self.params.get("sampling_method", None)
if self.sampling_method == "KMeansSMOTE":
smote = KMeansSMOTE(
sampling_strategy="minority",
k_neighbors=self.k_neighbors,
kmeans_estimator=self.kmeans_estimator,
cluster_balance_threshold=0.001,
random_state=self.seed,
n_jobs=-1,
)
x_train_fold, y_train_fold = smote.fit_resample(
x_train_fold, y_train_fold
)
y_train_fold = y_train_fold.astype(int)
elif self.sampling_method == "class_weight":
self.class_1_weight = int(
(y_train_fold.shape[0] - y_train_fold.sum()) / y_train_fold.sum()
)
self.class_weights = {0: 1, 1: self.class_1_weight}
self.model = lgb.LGBMClassifier(
boosting_type=self.boosting_type,
learning_rate=self.learning_rate,
num_leaves=self.num_leaves,
reg_alpha=self.l1_reg,
reg_lambda=self.l2_reg,
subsample=self.subsample,
subsample_freq=1,
colsample_bytree=self.tree_subsample,
class_weight=self.class_weights
if self.sampling_method == "class_weight"
else None,
n_estimators=100,
random_state=self.seed,
verbose=-1,
)
self.model.fit(x_train_fold, y_train_fold)
y_pred_val = self.model.predict(x_val)
val_metrics = get_metrics(y_val, y_pred_val)
fold_results.append(val_metrics)
return average_fold_results(fold_results)
def eval(self, x_test=None, y_test=None):
if x_test is not None and y_test is not None:
self.x_test = x_test
self.y_test = y_test
self.y_pred_test = self.model.predict(self.x_test)
test_metrics = get_metrics(self.y_test, self.y_pred_test)
return test_metrics
def tune(self):
scaling_methods = [
"standard_scaling",
"robust_scaling",
"minmax_scaling",
"yeo_johnson",
]
sampling_methods = [
"KMeansSMOTE",
"class_weight",
]
boosting_type_list = ["gbdt", "dart"]
learning_rate_list = [0.03, 0.05, 0.1]
number_of_leaves_list = [100]
l2_regularization_lambda_list = [0.1]
l1_regularization_alpha_list = [0.1]
tree_subsample_tree_list = [0.8, 1.0]
subsample_list = [0.8, 1.0]
kmeans_smote_k_neighbors_list = [10]
kmeans_smote_n_clusters_list = [5]
tuning_results = []
param_product = list(
product(
scaling_methods,
sampling_methods,
boosting_type_list,
learning_rate_list,
number_of_leaves_list,
l2_regularization_lambda_list,
l1_regularization_alpha_list,
tree_subsample_tree_list,
subsample_list,
kmeans_smote_k_neighbors_list,
kmeans_smote_n_clusters_list,
)
)
for (
scaling_method,
sampling_method,
boosting_type,
learning_rate,
num_leaves,
l2_reg,
l1_reg,
tree_subsample,
subsample,
k_neighbors,
kmeans_estimator,
) in tqdm(param_product, total=len(param_product), desc=" > LightGBM Tuning: "):
self.scaling_method = scaling_method
self.sampling_method = sampling_method
self.boosting_type = boosting_type
self.learning_rate = learning_rate
self.num_leaves = num_leaves
self.l2_reg = l2_reg
self.l1_reg = l1_reg
self.tree_subsample = tree_subsample
self.subsample = subsample
self.k_neighbors = k_neighbors
self.kmeans_estimator = kmeans_estimator
print(
" >> Fitting Params: ",
scaling_method,
sampling_method,
boosting_type,
learning_rate,
num_leaves,
l2_reg,
l1_reg,
tree_subsample,
subsample,
k_neighbors,
kmeans_estimator,
)
self.preprocess()
fold_result = self.fit()
tuning_results.append(
{
"model": "lightgbm",
"scaling_method": scaling_method,
"sampling_method": sampling_method,
"boosting_type": boosting_type,
"learning_rate": learning_rate,
"num_leaves": num_leaves,
"l2_reg": l2_reg,
"l1_reg": l1_reg,
"tree_subsample": tree_subsample,
"subsample": subsample,
"k_neighbors": k_neighbors,
"kmeans_estimator": kmeans_estimator,
"metrics": fold_result,
}
)
self.tuning_results = tuning_results
df_tuning = pandas.DataFrame(tuning_results)
metrics_df = df_tuning["metrics"].apply(pandas.Series)
df_tuning = pandas.concat(
[df_tuning.drop(columns=["metrics"]), metrics_df], axis=1
)
df_tuning.to_csv("lightgbm_tuning_results.csv", index=False)
return

176
models/model_utils.py Normal file
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@@ -0,0 +1,176 @@
def split_path(full_path):
import os
directory = os.path.dirname(full_path)
filename = os.path.splitext(os.path.basename(full_path))[0]
return directory, filename
def write_textfile(path, data_list):
with open(path, "w") as file:
for data in data_list:
file.write(f"{data} \n")
def missing_value_handler(data_path):
import pandas
from sklearn.impute import KNNImputer
data_directory, data_filename = split_path(data_path)
data_frame = pandas.read_csv(data_path)
columns = list(data_frame.head(0))
# remove column id
if "id" in columns:
data_frame = data_frame.drop("id", axis="columns")
columns = list(data_frame.head(0))
write_textfile(f"{data_directory}/columns.txt", columns)
# find missing values
missing_value_counts = data_frame.isna().sum()
write_textfile(f"{data_directory}/missing.txt", missing_value_counts)
# fill missing values - KNNImputer
imputer = KNNImputer(n_neighbors=5)
data_imputed = imputer.fit_transform(data_frame)
data_frame_imputed = pandas.DataFrame(data_imputed, columns=columns)
missing_value_counts = data_frame_imputed.isna().sum()
write_textfile(f"{data_directory}/no_missing.txt", missing_value_counts)
data_frame_imputed.to_csv("./data/Ketamine_icp_no_missing.csv", index=False)
return data_frame_imputed
def scaling_handler(data_frame, method="robust_scaling"):
import pandas
from sklearn.preprocessing import (
MaxAbsScaler,
MinMaxScaler,
PowerTransformer,
QuantileTransformer,
RobustScaler,
StandardScaler,
)
# Separate features and label
labels = data_frame["label"]
X = data_frame.drop("label", axis=1)
# Choose scaler/transformer
if method == "robust_scaling":
scaler = RobustScaler()
elif method == "standard_scaling":
scaler = StandardScaler()
elif method == "minmax_scaling":
scaler = MinMaxScaler()
elif method == "maxabs_scaling":
scaler = MaxAbsScaler()
elif method == "quantile_normal":
scaler = QuantileTransformer(output_distribution="normal", random_state=42)
elif method == "quantile_uniform":
scaler = QuantileTransformer(output_distribution="uniform", random_state=42)
elif method == "yeo_johnson":
scaler = PowerTransformer(method="yeo-johnson")
elif method == "box_cox":
# Box-Cox requires all positive values
scaler = PowerTransformer(
method="box-cox",
)
X_pos = X.copy()
min_per_column = X_pos.min()
for col in X_pos.columns:
if min_per_column[col] <= 0:
X_pos[col] = X_pos[col] + abs(min_per_column[col]) + 1e-6 # tiny offset
X = X_pos
else:
raise ValueError(f"Unknown scaling method: {method}")
# Fit and transform
X_scaled = scaler.fit_transform(X)
data_frame_scaled = pandas.DataFrame(X_scaled, columns=X.columns)
data_frame_scaled["label"] = labels.values
return data_frame_scaled
from sklearn.metrics import (
accuracy_score,
f1_score,
fbeta_score,
precision_score,
recall_score,
)
def get_metrics(y_true, y_pred, prefix=""):
metrics = {}
metrics[f"{prefix}accuracy"] = accuracy_score(y_true, y_pred)
metrics[f"{prefix}f1_macro"] = f1_score(y_true, y_pred, average="macro")
metrics[f"{prefix}f2_macro"] = fbeta_score(y_true, y_pred, beta=2, average="macro")
metrics[f"{prefix}recall_macro"] = recall_score(y_true, y_pred, average="macro")
metrics[f"{prefix}precision_macro"] = precision_score(
y_true, y_pred, average="macro"
)
# Per-class scores
f1_scores = f1_score(y_true, y_pred, average=None, zero_division=0)
f2_scores = fbeta_score(y_true, y_pred, beta=2, average=None, zero_division=0)
recall_scores = recall_score(y_true, y_pred, average=None, zero_division=0)
precision_scores = precision_score(y_true, y_pred, average=None, zero_division=0)
for i in range(len(f1_scores)):
metrics[f"{prefix}f1_class{i}"] = f1_scores[i]
metrics[f"{prefix}f2_class{i}"] = f2_scores[i]
metrics[f"{prefix}recall_class{i}"] = recall_scores[i]
metrics[f"{prefix}precision_class{i}"] = precision_scores[i]
# Confusion-matrix components
TP = sum((y_true == 1) & (y_pred == 1))
TN = sum((y_true == 0) & (y_pred == 0))
FP = sum((y_true == 0) & (y_pred == 1))
FN = sum((y_true == 1) & (y_pred == 0))
metrics[f"{prefix}TP"] = TP
metrics[f"{prefix}TN"] = TN
metrics[f"{prefix}FP"] = FP
metrics[f"{prefix}FN"] = FN
return metrics
import numpy as np
import pandas as pd
def average_fold_results(fold_results):
"""
Computes the average of metrics over multiple folds.
fold_results: list of dictionaries, each containing metrics for one fold
Returns:
dict of average metrics
"""
if not fold_results:
return {}
# Convert list of dicts to DataFrame
df = pd.DataFrame(fold_results)
# Compute mean for each column
avg_metrics = df.mean().to_dict()
# Convert any NumPy types to float
for k, v in avg_metrics.items():
if isinstance(v, (np.float32, np.float64)):
avg_metrics[k] = float(v)
return avg_metrics

193
results/results_catboost_tuning.csv Normal file
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@@ -0,0 +1,193 @@
iteration,model,params,avg_val_accuracy,test_accuracy,avg_val_f1_macro,test_f1_macro,avg_val_f2_macro,test_f2_macro,avg_val_recall_macro,test_recall_macro,avg_val_precision_macro,test_precision_macro,avg_val_f1_class0,test_f1_class0,avg_val_f1_class1,test_f1_class1,avg_val_f2_class0,test_f2_class0,avg_val_f2_class1,test_f2_class1,avg_val_recall_class0,test_recall_class0,avg_val_recall_class1,test_recall_class1,avg_val_precision_class0,test_precision_class0,avg_val_precision_class1,test_precision_class1,avg_val_TP,test_TP,avg_val_TN,test_TN,avg_val_FP,test_FP,avg_val_FN,test_FN
0,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'}",0.9836959985918459,0.9818884818884819,0.8503833428328909,0.8311370850445974,0.8421460012891915,0.8208106409719313,0.8373086437903854,0.8143042243859682,0.8670305978904527,0.8500362385081488,0.9916120128599493,0.9906874542220362,0.7091546728058324,0.6715867158671587,0.9922722721012012,0.9916212819438626,0.692019730477182,0.65,0.9927140593007311,0.9922448124083001,0.6819032282800398,0.6363636363636364,0.9905180222167175,0.9891349770162975,0.7435431735641876,0.7109375,110.2,91,5368.2,4734,39.4,37,51.4,52
1,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'}",0.9777704934310316,0.9861619861619861,0.833228302428493,0.8499051328858289,0.8671302415669343,0.8082749335304329,0.8942706966433456,0.7859796878870449,0.7898273786889105,0.9543529137800547,0.9884877444816569,0.9929137140475198,0.6779688603753289,0.7068965517241379,0.9851352071475208,0.9962775523861307,0.7491252759863476,0.6202723146747352,0.982912990556839,0.9985328023475163,0.805628402729852,0.5734265734265734,0.9941271433996267,0.9873575129533678,0.5855276139781941,0.9213483146067416,130.2,82,5315.2,4764,92.4,7,31.4,61
2,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'}",0.9835164073498687,0.9818884818884819,0.8490581437874211,0.8311370850445974,0.8415838131705449,0.8208106409719313,0.8372085097859262,0.8143042243859682,0.8642175021285254,0.8500362385081488,0.9915190916955767,0.9906874542220362,0.7065971958792655,0.6715867158671587,0.9921242029264252,0.9916212819438626,0.6910434234146647,0.65,0.9925291275390329,0.9922448124083001,0.6818878920328195,0.6363636363636364,0.9905166269662289,0.9891349770162975,0.7379183772908221,0.7109375,110.2,91,5367.2,4734,40.4,37,51.4,52
3,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'}",0.9792428656582848,0.9855514855514855,0.8426154736989677,0.8439396413099634,0.8748879195200697,0.8038417154985349,0.9004295319179321,0.7822735847259008,0.800633879546418,0.9435737976782753,0.989254353849541,0.9926003126628452,0.6959765935483945,0.6952789699570815,0.986153005125123,0.9959009536556801,0.7636228339150163,0.6117824773413897,0.9840965456240705,0.9981136030182352,0.8167625182117936,0.5664335664335665,0.9944681924419113,0.9871475953565506,0.6067995666509246,0.9,132.0,81,5321.6,4762,86.0,9,29.6,62
4,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'}",0.9830854631611965,0.9806674806674807,0.8444466123357083,0.8247747610795253,0.8359448119437027,0.8201022404248155,0.8309910233274715,0.8170671290562299,0.8617840830986598,0.8328983330460691,0.9912983540257126,0.9900513142737459,0.6975948706457037,0.6594982078853047,0.9919913642963373,0.9904869667253373,0.6798982595910681,0.6497175141242938,0.9924551698816895,0.9907776147558164,0.6695268767732535,0.6433566433566433,0.9901505152964749,0.9893260778568439,0.7334176509008448,0.6764705882352942,108.2,92,5366.8,4727,40.8,44,53.4,51
5,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'}",0.9766213003311162,0.9861619861619861,0.8282667296709023,0.8499051328858289,0.8655558235615745,0.8082749335304329,0.8960856336387624,0.7859796878870449,0.7818694784280174,0.9543529137800547,0.9878828804471741,0.9929137140475198,0.6686505788946306,0.7068965517241379,0.9840922083904875,0.9962775523861307,0.7470194387326615,0.6202723146747352,0.9815815885719037,0.9985328023475163,0.8105896787056208,0.5734265734265734,0.9942675424302492,0.9873575129533678,0.5694714144257854,0.9213483146067416,131.0,82,5308.0,4764,99.6,7,30.6,61
6,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'}",0.9834445876168182,0.9814814814814815,0.8480565198950298,0.8273424127984086,0.8399982205343853,0.817134478424801,0.8353828069223974,0.8107029210571445,0.8648736951754852,0.8460255171333055,0.991482470473956,0.9904781835303965,0.7046305693161037,0.6642066420664207,0.9921315087105121,0.9914118139924591,0.6878649323582586,0.6428571428571429,0.9925660892684587,0.9920352127436596,0.6781995245763361,0.6293706293706294,0.9904082761921845,0.988926034266611,0.739339114158786,0.703125,109.6,90,5367.4,4733,40.2,38,52.0,53
7,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'}",0.9772677101665316,0.9861619861619861,0.8335820375575876,0.8511569731081927,0.8723791821004074,0.8110472197412031,0.9042296019514786,0.789371391551228,0.7855109502194862,0.9498237611445158,0.9882165504467866,0.9929122368146759,0.6789475246683887,0.7094017094017094,0.9843362470693189,0.9961517547161919,0.760422117131496,0.6259426847662142,0.981766465616016,0.9983232026828757,0.8266927382869411,0.5804195804195804,0.9947529994233643,0.9875596102011196,0.5762689010156086,0.9120879120879121,133.6,83,5309.0,4763,98.6,8,28.0,60
8,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'}",0.985096544638456,0.9835164835164835,0.8585729444489173,0.8429347386448162,0.8434482396648738,0.8278311019035429,0.8344179216671094,0.8185343267087136,0.889046398570961,0.8717278113796217,0.9923402668444922,0.9915298546481229,0.7248056220533428,0.6943396226415094,0.993561646415106,0.9928379963142905,0.6933348329146416,0.6628242074927954,0.9943783630796371,0.9937120100607839,0.6744574802545816,0.6433566433566433,0.9903144308489432,0.9893572621035058,0.7877783662929788,0.7540983606557377,109.0,92,5377.2,4741,30.4,30,52.6,51
9,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'}",0.9844141443416088,0.9861619861619861,0.860822728291027,0.8486315083758391,0.8591203649369745,0.8054854277835695,0.8580872070377591,0.7825879842228616,0.8640595706246244,0.959095032968289,0.9919755925243938,0.9929151906647218,0.7296698640576607,0.7043478260869566,0.992107799790999,0.9964033290117519,0.7261329300829503,0.6145675265553869,0.9921961915465707,0.9987424020121568,0.7239782225289472,0.5664335664335665,0.991756262138165,0.9871555831779574,0.7363628791110837,0.9310344827586207,117.0,81,5365.4,4765,42.2,6,44.6,62
10,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'}",0.9849887795771217,0.9839234839234839,0.8592497644352056,0.8479285580818141,0.8454188939444627,0.8341042628974531,0.8373755895748365,0.8255273337017206,0.8879885654105774,0.8739174355175432,0.9922816875173123,0.9917372659763624,0.726217841353099,0.704119850187266,0.9933605268760685,0.992921169473067,0.697477261012857,0.6752873563218391,0.994082518770868,0.9937120100607839,0.6806686603788052,0.6573426573426573,0.9904946323445923,0.9897703549060543,0.7854824984765625,0.7580645161290323,110.0,94,5375.6,4741,32.0,30,51.6,49
11,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'}",0.9848451272158361,0.9857549857549858,0.8633896572691946,0.844179493916305,0.8595086205883866,0.8015872466872441,0.8571115490746607,0.7789866808940379,0.870647713252939,0.953244322524878,0.9921996367898099,0.9927068139195666,0.734579677748579,0.6956521739130435,0.9925079856313637,0.9961942202333653,0.7265092555454098,0.6069802731411229,0.9927140114228432,0.9985328023475163,0.7215090867264781,0.5594405594405595,0.991687560074482,0.9869484151646986,0.7496078664313959,0.9195402298850575,116.6,80,5368.2,4764,39.4,7,45.0,63
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22,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.05, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'}",0.9848810338585648,0.9839234839234839,0.8572385245926517,0.8490276198961566,0.8429098030071417,0.8366553327392703,0.8343031271803456,0.8289190373659039,0.885742499312083,0.8719715956558062,0.9922284455454948,0.9917355371900827,0.7222486036398086,0.7063197026022305,0.9933839125983333,0.9927949061662198,0.69243569341595,0.6805157593123209,0.9941564422297198,0.9935024103961434,0.6744498121309714,0.6643356643356644,0.9903114927254559,0.9899749373433584,0.7811735058987099,0.753968253968254,109.0,95,5376.0,4740,31.6,31,52.6,48
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183,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'yeo_johnson'}",0.9869280155309609,0.985958485958486,0.8826676504459643,0.8470544772514138,0.8789456300777816,0.8049361241017452,0.8767811321344683,0.7824831843905413,0.890261538812334,0.953803733564405,0.9932706480038191,0.9928102532041263,0.7720646528881092,0.7012987012987013,0.9935353631066063,0.9962358845671268,0.7643558970489572,0.6136363636363636,0.9937125594916955,0.9985328023475163,0.7598497047772412,0.5664335664335665,0.9928323286968121,0.9871529216742644,0.7876907489278562,0.9204545454545454,122.8,81,5373.6,4764,34.0,7,38.8,62
184,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'yeo_johnson'}",0.9869639415164674,0.9845339845339846,0.8726840371150519,0.8487424363927973,0.851149342600672,0.828051489000142,0.838388794706144,0.8156666222061317,0.9168367617602247,0.8902343785390072,0.9933057246808102,0.9920585161964472,0.7520623495492934,0.7054263565891473,0.9949901004453066,0.9938031235606917,0.7073085847560374,0.6622998544395924,0.9961165971570931,0.9949696080486271,0.6806609922551952,0.6363636363636364,0.9905126540199793,0.9891644092519275,0.8431608695004702,0.7913043478260869,110.0,91,5386.6,4747,21.0,24,51.6,52
185,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'yeo_johnson'}",0.9876103642470679,0.9861619861619861,0.8788810270598635,0.8499051328858289,0.8571088017964599,0.8082749335304329,0.8441222794051564,0.7859796878870449,0.9229738792894443,0.9543529137800547,0.9936379645982708,0.9929137140475198,0.7641240895214564,0.7068965517241379,0.9953228213453992,0.9962775523861307,0.7188947822475205,0.6202723146747352,0.9964494510731761,0.9985328023475163,0.6917951077371367,0.5734265734265734,0.9908433514173332,0.9873575129533678,0.8551044071615552,0.9213483146067416,111.8,82,5388.4,4764,19.2,7,49.8,61
186,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'yeo_johnson'}",0.9861379726770372,0.9841269841269841,0.8655409029545755,0.8459239130434782,0.8456436922034275,0.8269224843623216,0.8337565922973423,0.8154570225414912,0.9057043723562375,0.8834688346883469,0.992880278067109,0.9918478260869565,0.7382015278420421,0.7,0.9944651338532046,0.9934676102340773,0.6968222505536504,0.660377358490566,0.995524840142572,0.9945504087193461,0.6719883444521126,0.6363636363636364,0.990251316203827,0.989159891598916,0.8211574285086479,0.7777777777777778,108.6,91,5383.4,4745,24.2,26,53.0,52
187,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'yeo_johnson'}",0.9876103255615127,0.9867724867724867,0.8791891827878047,0.8559208843672739,0.8579827803715039,0.8127209992015513,0.8453160241563669,0.7896857910481889,0.9220405870309436,0.965374580868779,0.9936374673126374,0.9932270501198291,0.7647408982629722,0.7186147186147186,0.995278311289981,0.9966541196152238,0.7206872494530266,0.6287878787878788,0.996375472896738,0.9989520016767973,0.6942565754159957,0.5804195804195804,0.9909157071099024,0.9875673435557397,0.8531654669519847,0.9431818181818182,112.2,83,5388.0,4766,19.6,5,49.4,60
188,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'yeo_johnson'}",0.9860302656440355,0.9827024827024827,0.8649460777272889,0.8351784294420911,0.8461713033990161,0.8204170756400867,0.834914033984797,0.8113317200510661,0.9026337700286632,0.863322408932921,0.9928242792239658,0.9911115758653143,0.7370678762306121,0.6792452830188679,0.9943319241497942,0.9924191656893953,0.6980106826482381,0.6484149855907781,0.995339915220572,0.9932928107315029,0.6744881527490223,0.6293706293706294,0.9903230433373681,0.9889398998330551,0.8149444967199582,0.7377049180327869,109.0,90,5382.4,4739,25.2,32,52.6,53
189,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'yeo_johnson'}",0.9869280219785533,0.9865689865689866,0.8763001326976253,0.8555347091932457,0.8613527103869986,0.814922530688772,0.8521808415878034,0.7929726948800518,0.9050037909455038,0.955421936554012,0.9932814075310376,0.9931207004377736,0.7593188578642132,0.717948717948718,0.9944488685799555,0.9963608984816162,0.7282565521940416,0.6334841628959276,0.995228947955915,0.9985328023475163,0.7091327352196918,0.5874125874125874,0.9913426930080359,0.9877669500311009,0.8186648888829717,0.9230769230769231,114.6,84,5381.8,4764,25.8,7,47.0,59
190,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'yeo_johnson'}",0.9858866132827497,0.9835164835164835,0.8650030578546822,0.8452055343239073,0.8484471242242065,0.8329689456470806,0.838432819606199,0.8253177340370801,0.8976549194888044,0.8678989139515456,0.9927482957618589,0.9915263102835025,0.7372578199475056,0.6988847583643123,0.9940799505450167,0.9925854557640751,0.7028142979033961,0.673352435530086,0.9949700790559686,0.9932928107315029,0.6818955601564297,0.6573426573426573,0.9905382524089305,0.9897660818713451,0.8047715865686783,0.746031746031746,110.2,94,5380.4,4739,27.2,32,51.4,49
191,CatBoostClassifier,"{'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'yeo_johnson'}",0.9867124918558847,0.9863654863654864,0.8763060935692055,0.8502218435235476,0.8645769967271869,0.8060361396040803,0.8574628643412587,0.7826927840551818,0.8992122472214948,0.9645093543477004,0.9931670881582635,0.9930201062610688,0.7594450989801474,0.7074235807860262,0.994070409898111,0.9965707594513216,0.7350835835562627,0.6155015197568389,0.9946742142281044,0.9989520016767973,0.7202515144544129,0.5664335664335665,0.9916678816816621,0.9871582435791217,0.8067566127613276,0.9418604651162791,116.4,81,5378.8,4766,28.8,5,45.2,62
1 iteration model params avg_val_accuracy test_accuracy avg_val_f1_macro test_f1_macro avg_val_f2_macro test_f2_macro avg_val_recall_macro test_recall_macro avg_val_precision_macro test_precision_macro avg_val_f1_class0 test_f1_class0 avg_val_f1_class1 test_f1_class1 avg_val_f2_class0 test_f2_class0 avg_val_f2_class1 test_f2_class1 avg_val_recall_class0 test_recall_class0 avg_val_recall_class1 test_recall_class1 avg_val_precision_class0 test_precision_class0 avg_val_precision_class1 test_precision_class1 avg_val_TP test_TP avg_val_TN test_TN avg_val_FP test_FP avg_val_FN test_FN
2 0 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9836959985918459 0.9818884818884819 0.8503833428328909 0.8311370850445974 0.8421460012891915 0.8208106409719313 0.8373086437903854 0.8143042243859682 0.8670305978904527 0.8500362385081488 0.9916120128599493 0.9906874542220362 0.7091546728058324 0.6715867158671587 0.9922722721012012 0.9916212819438626 0.692019730477182 0.65 0.9927140593007311 0.9922448124083001 0.6819032282800398 0.6363636363636364 0.9905180222167175 0.9891349770162975 0.7435431735641876 0.7109375 110.2 91 5368.2 4734 39.4 37 51.4 52
3 1 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9777704934310316 0.9861619861619861 0.833228302428493 0.8499051328858289 0.8671302415669343 0.8082749335304329 0.8942706966433456 0.7859796878870449 0.7898273786889105 0.9543529137800547 0.9884877444816569 0.9929137140475198 0.6779688603753289 0.7068965517241379 0.9851352071475208 0.9962775523861307 0.7491252759863476 0.6202723146747352 0.982912990556839 0.9985328023475163 0.805628402729852 0.5734265734265734 0.9941271433996267 0.9873575129533678 0.5855276139781941 0.9213483146067416 130.2 82 5315.2 4764 92.4 7 31.4 61
4 2 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9835164073498687 0.9818884818884819 0.8490581437874211 0.8311370850445974 0.8415838131705449 0.8208106409719313 0.8372085097859262 0.8143042243859682 0.8642175021285254 0.8500362385081488 0.9915190916955767 0.9906874542220362 0.7065971958792655 0.6715867158671587 0.9921242029264252 0.9916212819438626 0.6910434234146647 0.65 0.9925291275390329 0.9922448124083001 0.6818878920328195 0.6363636363636364 0.9905166269662289 0.9891349770162975 0.7379183772908221 0.7109375 110.2 91 5367.2 4734 40.4 37 51.4 52
5 3 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9792428656582848 0.9855514855514855 0.8426154736989677 0.8439396413099634 0.8748879195200697 0.8038417154985349 0.9004295319179321 0.7822735847259008 0.800633879546418 0.9435737976782753 0.989254353849541 0.9926003126628452 0.6959765935483945 0.6952789699570815 0.986153005125123 0.9959009536556801 0.7636228339150163 0.6117824773413897 0.9840965456240705 0.9981136030182352 0.8167625182117936 0.5664335664335665 0.9944681924419113 0.9871475953565506 0.6067995666509246 0.9 132.0 81 5321.6 4762 86.0 9 29.6 62
6 4 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9830854631611965 0.9806674806674807 0.8444466123357083 0.8247747610795253 0.8359448119437027 0.8201022404248155 0.8309910233274715 0.8170671290562299 0.8617840830986598 0.8328983330460691 0.9912983540257126 0.9900513142737459 0.6975948706457037 0.6594982078853047 0.9919913642963373 0.9904869667253373 0.6798982595910681 0.6497175141242938 0.9924551698816895 0.9907776147558164 0.6695268767732535 0.6433566433566433 0.9901505152964749 0.9893260778568439 0.7334176509008448 0.6764705882352942 108.2 92 5366.8 4727 40.8 44 53.4 51
7 5 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9766213003311162 0.9861619861619861 0.8282667296709023 0.8499051328858289 0.8655558235615745 0.8082749335304329 0.8960856336387624 0.7859796878870449 0.7818694784280174 0.9543529137800547 0.9878828804471741 0.9929137140475198 0.6686505788946306 0.7068965517241379 0.9840922083904875 0.9962775523861307 0.7470194387326615 0.6202723146747352 0.9815815885719037 0.9985328023475163 0.8105896787056208 0.5734265734265734 0.9942675424302492 0.9873575129533678 0.5694714144257854 0.9213483146067416 131.0 82 5308.0 4764 99.6 7 30.6 61
8 6 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9834445876168182 0.9814814814814815 0.8480565198950298 0.8273424127984086 0.8399982205343853 0.817134478424801 0.8353828069223974 0.8107029210571445 0.8648736951754852 0.8460255171333055 0.991482470473956 0.9904781835303965 0.7046305693161037 0.6642066420664207 0.9921315087105121 0.9914118139924591 0.6878649323582586 0.6428571428571429 0.9925660892684587 0.9920352127436596 0.6781995245763361 0.6293706293706294 0.9904082761921845 0.988926034266611 0.739339114158786 0.703125 109.6 90 5367.4 4733 40.2 38 52.0 53
9 7 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9772677101665316 0.9861619861619861 0.8335820375575876 0.8511569731081927 0.8723791821004074 0.8110472197412031 0.9042296019514786 0.789371391551228 0.7855109502194862 0.9498237611445158 0.9882165504467866 0.9929122368146759 0.6789475246683887 0.7094017094017094 0.9843362470693189 0.9961517547161919 0.760422117131496 0.6259426847662142 0.981766465616016 0.9983232026828757 0.8266927382869411 0.5804195804195804 0.9947529994233643 0.9875596102011196 0.5762689010156086 0.9120879120879121 133.6 83 5309.0 4763 98.6 8 28.0 60
10 8 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.985096544638456 0.9835164835164835 0.8585729444489173 0.8429347386448162 0.8434482396648738 0.8278311019035429 0.8344179216671094 0.8185343267087136 0.889046398570961 0.8717278113796217 0.9923402668444922 0.9915298546481229 0.7248056220533428 0.6943396226415094 0.993561646415106 0.9928379963142905 0.6933348329146416 0.6628242074927954 0.9943783630796371 0.9937120100607839 0.6744574802545816 0.6433566433566433 0.9903144308489432 0.9893572621035058 0.7877783662929788 0.7540983606557377 109.0 92 5377.2 4741 30.4 30 52.6 51
11 9 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9844141443416088 0.9861619861619861 0.860822728291027 0.8486315083758391 0.8591203649369745 0.8054854277835695 0.8580872070377591 0.7825879842228616 0.8640595706246244 0.959095032968289 0.9919755925243938 0.9929151906647218 0.7296698640576607 0.7043478260869566 0.992107799790999 0.9964033290117519 0.7261329300829503 0.6145675265553869 0.9921961915465707 0.9987424020121568 0.7239782225289472 0.5664335664335665 0.991756262138165 0.9871555831779574 0.7363628791110837 0.9310344827586207 117.0 81 5365.4 4765 42.2 6 44.6 62
12 10 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9849887795771217 0.9839234839234839 0.8592497644352056 0.8479285580818141 0.8454188939444627 0.8341042628974531 0.8373755895748365 0.8255273337017206 0.8879885654105774 0.8739174355175432 0.9922816875173123 0.9917372659763624 0.726217841353099 0.704119850187266 0.9933605268760685 0.992921169473067 0.697477261012857 0.6752873563218391 0.994082518770868 0.9937120100607839 0.6806686603788052 0.6573426573426573 0.9904946323445923 0.9897703549060543 0.7854824984765625 0.7580645161290323 110.0 94 5375.6 4741 32.0 30 51.6 49
13 11 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9848451272158361 0.9857549857549858 0.8633896572691946 0.844179493916305 0.8595086205883866 0.8015872466872441 0.8571115490746607 0.7789866808940379 0.870647713252939 0.953244322524878 0.9921996367898099 0.9927068139195666 0.734579677748579 0.6956521739130435 0.9925079856313637 0.9961942202333653 0.7265092555454098 0.6069802731411229 0.9927140114228432 0.9985328023475163 0.7215090867264781 0.5594405594405595 0.991687560074482 0.9869484151646986 0.7496078664313959 0.9195402298850575 116.6 80 5368.2 4764 39.4 7 45.0 63
14 12 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9845578482836348 0.9829059829059829 0.8563663299732726 0.8388902766502218 0.8454671199718229 0.8261528709939931 0.8389500335245936 0.8182199272117527 0.8780677288206494 0.8626752975569012 0.9920587232276177 0.9912133891213389 0.7206739367189277 0.6865671641791045 0.9929390378954386 0.9923344363925773 0.697995202048207 0.6599713055954088 0.993527702966678 0.9930832110668623 0.684372364082509 0.6433566433566433 0.99059865579935 0.9893505951138024 0.765536801841949 0.736 110.6 92 5372.6 4738 35.0 33 51.0 51
15 13 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9835881690545862 0.9861619861619861 0.8604440218282645 0.8499051328858289 0.8690024937032241 0.8082749335304329 0.8750690128468441 0.7859796878870449 0.8474268563811116 0.9543529137800547 0.9915373100710676 0.9929137140475198 0.7293507335854615 0.7068965517241379 0.9907781531251416 0.9962775523861307 0.7472268342813067 0.6202723146747352 0.9902729846692268 0.9985328023475163 0.7598650410244614 0.5734265734265734 0.9928063346703722 0.9873575129533678 0.7020473780918509 0.9213483146067416 122.8 82 5355.0 4764 52.6 7 38.8 61
16 14 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9843782892796202 0.9831094831094831 0.8548343786951275 0.842521101783164 0.8444363446947081 0.8318391286133222 0.8382511272128722 0.8251081343724396 0.875660568579242 0.8620684026326786 0.9919660968482618 0.9913152662969551 0.7177026605419935 0.6937269372693727 0.9928132306858364 0.9922496857980729 0.6960594587035798 0.6714285714285714 0.99337979449169 0.9928736114022217 0.6831224599340542 0.6573426573426573 0.9905614094093099 0.9897618052653573 0.7607597277491742 0.734375 110.4 94 5371.8 4737 35.8 34 51.2 49
17 15 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9834445231408931 0.9857549857549858 0.8585152131064107 0.844179493916305 0.8660243812195902 0.8015872466872441 0.871386989342932 0.7789866808940379 0.8472268537593868 0.953244322524878 0.9914645120649892 0.9927068139195666 0.7255659141478319 0.6956521739130435 0.990793456215173 0.9961942202333653 0.7412553062240076 0.6069802731411229 0.9903470175632508 0.9985328023475163 0.7524269611226132 0.5594405594405595 0.9925866849189425 0.9869484151646986 0.7018670225998311 0.9195402298850575 121.6 80 5355.4 4764 52.2 7 40.0 63
18 16 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9852042839094203 0.9829059829059829 0.8591013566121541 0.8377128766278801 0.843357341174432 0.8235684959885494 0.8338746022135336 0.8148282235475697 0.8903497870028734 0.8644918571915838 0.9923966195989122 0.9912152269399708 0.7258060936253962 0.6842105263157895 0.9936730069527971 0.9924607329842932 0.6930416753960669 0.6546762589928058 0.99452629207372 0.9932928107315029 0.6732229123533471 0.6363636363636364 0.990278694812452 0.9891463160091839 0.7904208791932946 0.7398373983739838 108.8 91 5378.0 4739 29.6 32 52.8 52
19 17 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9854556110657452 0.9861619861619861 0.8742479754482927 0.8486315083758391 0.8795206560822282 0.8054854277835695 0.8832314698688688 0.7825879842228616 0.8661556775500792 0.959095032968289 0.9925042501994371 0.9929151906647218 0.7559917006971485 0.7043478260869566 0.9920528534779128 0.9964033290117519 0.7669884586865434 0.6145675265553869 0.9917524114040205 0.9987424020121568 0.7747105283337167 0.5664335664335665 0.9932585419218753 0.9871555831779574 0.7390528131782832 0.9310344827586207 125.2 81 5363.0 4765 44.6 6 36.4 62
20 18 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.985527450141573 0.9829059829059829 0.8612353081714964 0.8411930908822298 0.8438576249283887 0.8312762678885206 0.8334345750613062 0.8250033345401193 0.8960232034579164 0.859220918082185 0.9925641484406432 0.9912097111762244 0.7299064679023494 0.6911764705882353 0.9939617348758512 0.9920817797142737 0.693753514980926 0.6704707560627675 0.99489614191772 0.9926640117375812 0.6719730082048921 0.6573426573426573 0.9902455045426208 0.9897596656217346 0.8018009023732121 0.7286821705426356 108.6 94 5380.0 4736 27.6 35 53.0 49
21 19 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9857069898028101 0.9867724867724867 0.875516842420696 0.857127840635882 0.8794196618303813 0.8154847231608375 0.8821479755792095 0.7930774947123721 0.8694794389240332 0.9605514096185739 0.9926355370392489 0.993225638353309 0.7583981478021433 0.721030042918455 0.9923051686408139 0.9965283587083821 0.7665341550199488 0.6344410876132931 0.9920852311216122 0.9987424020121568 0.772210720036807 0.5874125874125874 0.9931873864715641 0.9877694859038143 0.7457714913765023 0.9333333333333333 124.8 84 5364.8 4765 42.8 6 36.8 59
22 20 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9848451465586135 0.9831094831094831 0.8578980478544278 0.8413834487516582 0.8449995607616705 0.8292825585548906 0.8372862614717818 0.8217164307082563 0.8837164915524758 0.8638262322472849 0.9922082995744397 0.9913170833769223 0.7235877961344157 0.6914498141263941 0.9932427909686832 0.9923760053619303 0.696756330554658 0.666189111747851 0.993934535059199 0.9930832110668623 0.6806379878843647 0.6503496503496503 0.9904924365174654 0.9895572263993316 0.7769405465874861 0.7380952380952381 110.0 93 5374.8 4738 32.8 33 51.6 50
23 21 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9845219029553508 0.986975986975987 0.8698030164940406 0.8610706662331604 0.8811907091671406 0.8215303999383525 0.8893450351340496 0.7999657018730588 0.8527575233127962 0.9564539548079303 0.9920159371267733 0.9933277731442869 0.7475900958613078 0.7288135593220338 0.9910359053245955 0.9964442585233215 0.7713455130096858 0.6466165413533834 0.9903839587735819 0.9985328023475163 0.7883061114945173 0.6013986013986014 0.9936549838375447 0.9881767268201618 0.7118600627880478 0.9247311827956989 127.4 86 5355.6 4764 52.0 7 34.2 57
24 22 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9848810338585648 0.9839234839234839 0.8572385245926517 0.8490276198961566 0.8429098030071417 0.8366553327392703 0.8343031271803456 0.8289190373659039 0.885742499312083 0.8719715956558062 0.9922284455454948 0.9917355371900827 0.7222486036398086 0.7063197026022305 0.9933839125983333 0.9927949061662198 0.69243569341595 0.6805157593123209 0.9941564422297198 0.9935024103961434 0.6744498121309714 0.6643356643356644 0.9903114927254559 0.9899749373433584 0.7811735058987099 0.753968253968254 109.0 95 5376.0 4740 31.6 31 52.6 48
25 23 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9857788159834528 0.9867724867724867 0.8784839889543908 0.8583142406586364 0.8862776919629682 0.818231408861414 0.8918001496056867 0.7964691983765553 0.8666283579401423 0.9559424197067787 0.9926680110948627 0.9932242259981237 0.7642999668139189 0.723404255319149 0.9920072029043858 0.9964025767589726 0.7805481810215507 0.6400602409638554 0.9915674796423222 0.9985328023475163 0.7920328195690514 0.5944055944055944 0.9937726356177532 0.9879717959352966 0.7394840802625315 0.9239130434782609 128.0 85 5362.0 4764 45.6 7 33.6 58
26 24 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9862816185907304 0.9843304843304843 0.8672185689164964 0.8461719843544885 0.8480914656258485 0.8248690951845485 0.8368322095688734 0.8121701187096282 0.9070127106465492 0.8892160087719299 0.9929532733995377 0.9919548636506113 0.7414838644334552 0.7003891050583657 0.9944720032893819 0.9937615139842573 0.701710927962315 0.6559766763848397 0.9954878989322411 0.9949696080486271 0.6781765202055057 0.6293706293706294 0.9904355947940818 0.9889583333333334 0.8235898264990166 0.7894736842105263 109.6 90 5383.2 4747 24.4 24 52.0 53
27 25 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.987287146434175 0.9865689865689866 0.8793207329883772 0.8543196878009516 0.8640834480409524 0.8121616449258658 0.8547686025189922 0.7895809912158687 0.9088091992486815 0.9600745182511498 0.9934665390741285 0.9931221342225928 0.7651749269026261 0.7155172413793104 0.9946559996735264 0.9964866786565728 0.7335108964083782 0.6278366111951589 0.9954508619661343 0.9987424020121568 0.7140863430718503 0.5804195804195804 0.9914916199405575 0.9875647668393782 0.8261267785568057 0.9325842696629213 115.4 83 5383.0 4765 24.6 6 46.2 60
28 26 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9861379726770372 0.9845339845339846 0.8651922681807562 0.8498745819397994 0.8446678192218264 0.8306556171204234 0.8325513658798114 0.819058325870315 0.907594390231754 0.8878465707734 0.9928805856772964 0.992056856187291 0.7375039506842161 0.7076923076923077 0.9945094105669959 0.9936769817009338 0.6948262278766573 0.6676342525399129 0.9955988593571996 0.9947600083839866 0.669503872402423 0.6433566433566433 0.9901800408376109 0.9893683552220137 0.8250087396258972 0.7863247863247863 108.2 92 5383.8 4746 23.8 25 53.4 51
29 27 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9871434682825193 0.9853479853479854 0.8772254950574203 0.8369587456890434 0.8601915470973859 0.79201146554352 0.8498851456211648 0.7686019702368476 0.9108052533200063 0.9570245378117729 0.9933937755480089 0.992501562174547 0.7610572145668317 0.6814159292035398 0.9947154089698568 0.9962366715450554 0.7256676852249151 0.5877862595419847 0.9955988319984066 0.9987424020121568 0.704171459243923 0.5384615384615384 0.9912003368948475 0.9863382322500518 0.8304101697451646 0.927710843373494 113.8 77 5383.8 4765 23.8 6 47.8 66
30 28 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9856352216504998 0.9839234839234839 0.8621834548618892 0.8468128932461787 0.8445478827269894 0.8315381150352711 0.834096523063451 0.8221356300375373 0.8982795995583649 0.8759305126029722 0.9926194370380234 0.9917389940395274 0.7317474726857547 0.7018867924528301 0.9940280522006834 0.9930474116267382 0.6950677132532957 0.670028818443804 0.9949701337735546 0.9939216097254244 0.6732229123533471 0.6503496503496503 0.9902836860643168 0.9895659432387313 0.806275513052413 0.7622950819672131 108.8 93 5380.4 4742 27.2 29 52.8 50
31 29 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9869639157260973 0.9863654863654864 0.879550626347441 0.8514876808093439 0.870045213129119 0.8088285616516482 0.8642020071527432 0.7860844877193651 0.897702092283037 0.959589157216592 0.9932950376100683 0.9930186516619777 0.7658062150848136 0.70995670995671 0.9940330947590708 0.9964450020911753 0.7460573314991672 0.6212121212121212 0.994526244195832 0.9987424020121568 0.7338777701096542 0.5734265734265734 0.9920693690150953 0.9873601326150021 0.8033348155509786 0.9318181818181818 118.6 82 5378.0 4765 29.6 6 43.0 61
32 30 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9854197237657939 0.9829059829059829 0.8621105186516613 0.8388902766502218 0.846693305689033 0.8261528709939931 0.8375860013996403 0.8182199272117527 0.8936165471770329 0.8626752975569012 0.9925054143077741 0.9912133891213389 0.7317156229955486 0.6865671641791045 0.993716351450437 0.9923344363925773 0.6996702599276289 0.6599713055954088 0.994526346791306 0.9930832110668623 0.6806456560079748 0.6433566433566433 0.9904979402930485 0.9893505951138024 0.7967351540610174 0.736 110.0 92 5378.0 4738 29.6 33 51.6 51
33 31 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.986820269812404 0.9865689865689866 0.877563416771528 0.8530835228353733 0.8666347927365601 0.8093836088798947 0.8599211737232284 0.7861892875516854 0.8984707203871171 0.9649457434116999 0.993222240007103 0.9931235674098771 0.761904593535953 0.7130434782608696 0.994070459172379 0.9966124377901384 0.7391991263007414 0.622154779969651 0.9946372251398856 0.9989520016767973 0.7252051223065716 0.5734265734265734 0.9918136647874025 0.9873627511912161 0.8051277759868316 0.9425287356321839 117.2 82 5378.6 4766 29.0 5 44.4 61
34 32 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9860302269584803 0.9847374847374848 0.8651027328460819 0.8512934504156158 0.8461482899592221 0.8312246938922571 0.8348833820094514 0.8191631257026353 0.9038301388756894 0.8912370096735709 0.9928238353193806 0.9921621904065211 0.7373816303727831 0.7104247104247104 0.9943316327358351 0.9938447366217235 0.6979649471826092 0.6686046511627907 0.9953399562587617 0.9949696080486271 0.6744268077601411 0.6433566433566433 0.9903232558576776 0.9893705710712797 0.8173370218937013 0.7931034482758621 109.0 92 5382.4 4747 25.2 24 52.6 51
35 33 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9870357419067401 0.9863654863654864 0.8786661233843809 0.8539546788327481 0.8667789278141586 0.8143617723634435 0.8594179708761187 0.7928678950477315 0.9011809877992263 0.9504039456837321 0.9933345981413282 0.9930157406442197 0.7639976486274334 0.7148936170212766 0.9942707099948773 0.9961934242449594 0.7392871456334397 0.6325301204819277 0.9948960598413411 0.9983232026828757 0.7239398819108964 0.5874125874125874 0.9917794881967399 0.9877644131065948 0.8105824874017126 0.9130434782608695 117.0 84 5380.0 4763 27.6 8 44.6 59
36 34 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9865330166705728 0.9853479853479854 0.8704343336721141 0.8567033607931763 0.852055150641523 0.8355388633123595 0.8411646823093639 0.8228692288637793 0.9081963631229814 0.8991384074580755 0.993081291115929 0.9924764890282132 0.7477873762282993 0.7209302325581395 0.9945233940238916 0.9942218314282125 0.7095869072591549 0.6768558951965066 0.9954878647337498 0.9953888073779082 0.6868414998849781 0.6503496503496503 0.9906899772878018 0.989581162742238 0.825702748958161 0.808695652173913 111.0 93 5383.2 4749 24.4 22 50.6 50
37 35 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9874307923478683 0.9865689865689866 0.8833724692667442 0.8530835228353733 0.8728278631569448 0.8093836088798947 0.8662311089632866 0.7861892875516854 0.9029529252661203 0.9649457434116999 0.9935361451475371 0.9931235674098771 0.7732087933859508 0.7130434782608696 0.9943515316814654 0.9966124377901384 0.751304194632424 0.622154779969651 0.9948960803604358 0.9989520016767973 0.7375661375661376 0.5734265734265734 0.9921809828073729 0.9873627511912161 0.8137248677248676 0.9425287356321839 119.2 82 5380.0 4766 27.6 5 42.4 61
38 36 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9857429609214643 0.9839234839234839 0.8650141053127711 0.8479285580818141 0.8498542333542429 0.8341042628974531 0.8407578673160888 0.8255273337017206 0.8953063331916912 0.8739174355175432 0.992672097027782 0.9917372659763624 0.7373561135977603 0.704119850187266 0.993871994366882 0.992921169473067 0.705836472341604 0.6752873563218391 0.9946742347471993 0.9937120100607839 0.6868414998849781 0.6573426573426573 0.990681450194724 0.9897703549060543 0.7999312161886583 0.7580645161290323 111.0 94 5378.8 4741 28.8 30 50.6 49
39 37 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9872153009107547 0.9877899877899878 0.8840021491512114 0.8708213212292216 0.8785517766085202 0.8319516140352616 0.8751326963288768 0.8105600121948896 0.8940398080556416 0.9628182304693047 0.9934209104863886 0.9937434827945777 0.7745833878160344 0.7478991596638656 0.9938397106639043 0.996736811278919 0.7632638425531365 0.6671664167916042 0.9941193915842164 0.9987424020121568 0.7561460010735374 0.6223776223776224 0.9927248278719031 0.9887943556754514 0.7953547882393799 0.9368421052631579 122.2 89 5375.8 4765 31.8 6 39.4 54
40 38 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9855633761270793 0.9831094831094831 0.8624888276327795 0.8413834487516582 0.846188359313875 0.8292825585548906 0.8364585393424646 0.8217164307082563 0.8953115577195039 0.8638262322472849 0.9925810317523748 0.9913170833769223 0.7323966235131841 0.6914498141263941 0.9938797735534243 0.9923760053619303 0.6984969450743261 0.666189111747851 0.9947482266030339 0.9930832110668623 0.6781688520818955 0.6503496503496503 0.9904269019149098 0.9895572263993316 0.8001962135240982 0.7380952380952381 109.6 93 5379.2 4738 28.4 33 52.0 50
41 39 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.986856176455133 0.986975986975987 0.8806864843228681 0.8610706662331604 0.8745665899447239 0.8215303999383525 0.8707409161538002 0.7999657018730588 0.8920152122815285 0.9564539548079303 0.9932361759186245 0.9933277731442869 0.7681367927271119 0.7288135593220338 0.9936992162752227 0.9964442585233215 0.755433963614225 0.6466165413533834 0.9940084790371456 0.9985328023475163 0.7474733532704546 0.6013986013986014 0.9924667122644513 0.9881767268201618 0.7915637122986056 0.9247311827956989 120.8 86 5375.2 4764 32.4 7 40.8 57
42 40 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9864611904899301 0.9839234839234839 0.8661749313206123 0.8456802466215746 0.8423831655399336 0.8289567585128061 0.8285185886091406 0.8187439263733541 0.9165958997953855 0.8780141843971632 0.9930497307842637 0.9917407213800313 0.7393001318569612 0.6996197718631179 0.9949315438410389 0.9931736326325488 0.6898347872388285 0.6647398843930635 0.9961906163717206 0.9941312093900649 0.6608465608465609 0.6433566433566433 0.9899313600303314 0.9893617021276596 0.8432604395604395 0.7666666666666667 106.8 92 5387.0 4743 20.6 28 54.8 51
43 41 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9878976560744542 0.9857549857549858 0.8831295105137231 0.844179493916305 0.8637043515732932 0.8015872466872441 0.8520621969448859 0.7789866808940379 0.9221681057118445 0.953244322524878 0.9937832463875461 0.9927068139195666 0.7724757746399004 0.6956521739130435 0.9952703152767622 0.9961942202333653 0.7321383878698244 0.6069802731411229 0.9962645671893655 0.9985328023475163 0.7078598267004065 0.5594405594405595 0.9913162166003714 0.9869484151646986 0.8530199948233175 0.9195402298850575 114.4 80 5387.4 4764 20.2 7 47.2 63
44 42 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9862456926052239 0.9849409849409849 0.8669992450376824 0.8538252508361204 0.8476890859443065 0.834388749878525 0.836226417544124 0.8226596291991387 0.9064155407810034 0.8922243068584532 0.9929348018352687 0.9922658862876255 0.7410636882400962 0.7153846153846154 0.9944647211564472 0.9938863531677903 0.7009134507321659 0.6748911465892597 0.9954878784131462 0.9949696080486271 0.6769649566751015 0.6503496503496503 0.9903976970023715 0.9895768188451115 0.8224333845596353 0.7948717948717948 109.4 93 5383.2 4747 24.4 24 52.2 50
45 43 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9869280090833683 0.9855514855514855 0.8733416353384016 0.8399148032946274 0.8540518352792473 0.7953952629034529 0.8425426933169042 0.7720984737333512 0.9124716978233179 0.9575569358178053 0.9932857625110962 0.9926049369857306 0.7533975081657068 0.6872246696035242 0.9948049328325747 0.9962783306849544 0.71329873772592 0.5945121951219512 0.9958207596880222 0.9987424020121568 0.6892646269457863 0.5454545454545454 0.990765918059411 0.9865424430641822 0.8341774775872247 0.9285714285714286 111.4 78 5385.0 4765 22.6 6 50.2 65
46 44 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9862097988576801 0.9839234839234839 0.8667386962508832 0.8433624344377819 0.8474929512704872 0.8237478893318506 0.8361964447535432 0.8119605190449877 0.9068725501572557 0.8824078998433256 0.992915992849021 0.9917441738948688 0.7405613996527456 0.694980694980695 0.9944348633360782 0.9934260112218407 0.7005510392048959 0.6540697674418605 0.9954509372028151 0.9945504087193461 0.6769419523042711 0.6293706293706294 0.9903984180691285 0.9889537307211338 0.8233466822453825 0.7758620689655172 109.4 90 5383.0 4745 24.6 26 52.2 53
47 45 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9875385187236475 0.9865689865689866 0.8818473760069663 0.8543196878009516 0.8666113884382227 0.8121616449258658 0.8573085650407084 0.7895809912158687 0.911358823985703 0.9600745182511498 0.9935954869318611 0.9931221342225928 0.7700992650820717 0.7155172413793104 0.9947741585846203 0.9964866786565728 0.738448618291825 0.6278366111951589 0.9955618429101876 0.9987424020121568 0.7190552871712291 0.5804195804195804 0.991638608632873 0.9875647668393782 0.831079039338533 0.9325842696629213 116.2 83 5383.6 4765 24.0 6 45.4 60
48 46 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'standard_scaling'} 0.9849169533964789 0.9829059829059829 0.857781434054387 0.8365176229040721 0.844304223769899 0.8209688288843962 0.836133326191199 0.8114365198833864 0.8841541341909854 0.8663719301391664 0.9922467381814037 0.9912170639899623 0.7233161299273703 0.6818181818181818 0.9933470205947339 0.992587008418143 0.695261426945064 0.6493506493506493 0.9940824640532819 0.9935024103961434 0.6781841883291158 0.6293706293706294 0.9904205833879198 0.9889422073857709 0.7778876849940513 0.743801652892562 109.6 90 5375.6 4740 32.0 31 52.0 53
49 47 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'standard_scaling'} 0.9873589597196328 0.986975986975987 0.8811716945986273 0.8599124452782989 0.8674250110669686 0.8187978416363408 0.8589932970703573 0.7965739982088755 0.9076249537472911 0.9610201119635082 0.9935015709609486 0.9933291640608714 0.7688418182363058 0.7264957264957265 0.9945592266059802 0.9965700422470406 0.7402907955279572 0.6410256410256411 0.9952659438838323 0.9987424020121568 0.7227206502568821 0.5944055944055944 0.9917452330433599 0.9879742898610823 0.8235046744512223 0.9340659340659341 116.8 85 5382.0 4765 25.6 6 44.8 58
50 48 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.982654512524932 0.9794464794464794 0.8404271891994972 0.8175151805953109 0.8329745957227697 0.8168681204876317 0.8283393664784457 0.8164383300623083 0.8543775057592086 0.8185999905552341 0.9910774933869468 0.9894163261029026 0.6897768850120474 0.6456140350877193 0.9917259815399992 0.9894785378940308 0.6742232099055401 0.6442577030812325 0.9921591409010674 0.9895200167679732 0.6645195920558239 0.6433566433566433 0.9900000029126585 0.9893126571668064 0.7187550086057587 0.647887323943662 107.4 92 5365.2 4721 42.4 50 54.2 51
51 49 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9782014634100736 0.9857549857549858 0.8359189878862748 0.8454905779707063 0.8691457018101014 0.8043882221350002 0.8956940508871784 0.7823783845582211 0.793225782868173 0.9486313093089597 0.988712124752085 0.9927052938724469 0.6831258510204649 0.6982758620689655 0.9854471586724532 0.9960684261156887 0.7528442449477495 0.6127080181543116 0.9832828951184253 0.9983232026828757 0.8081052066559312 0.5664335664335665 0.9942038470772385 0.9871502590673575 0.5922477186591077 0.9101123595505618 130.6 81 5317.2 4763 90.4 8 31.0 62
52 50 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9834446069595959 0.9798534798534798 0.8473635122196328 0.8211287413756019 0.8391527985232671 0.8204743164871582 0.8341506497363043 0.820039633391132 0.8632678487668674 0.8222258951867112 0.9914842548047833 0.9896259038038353 0.7032427696344824 0.6526315789473685 0.9921769661715769 0.9896881287726358 0.6861286308749573 0.6512605042016807 0.9926399238112331 0.9897296164326137 0.6756613756613756 0.6503496503496503 0.9903343162178494 0.9895222129086337 0.7362013813158851 0.6549295774647887 109.2 93 5367.8 4722 39.8 49 52.4 50
53 51 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9790633259970475 0.9855514855514855 0.841153558692153 0.8412798640324161 0.8728679732342892 0.7982282553760953 0.8979457458927058 0.7754901773975343 0.7998293090663922 0.9526743222673937 0.9891615992948537 0.9926033961871028 0.6931455180894525 0.6899563318777293 0.9860938113144261 0.9961525593844095 0.759642135154152 0.6003039513677811 0.9840595770549463 0.9985328023475163 0.8118319147304656 0.5524475524475524 0.9943189059935005 0.9867439933719967 0.605339712139284 0.9186046511627907 131.2 79 5321.4 4764 86.2 7 30.4 64
54 52 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9827263838387225 0.9784289784289785 0.8418163921473443 0.8104166666666667 0.8353764587972152 0.8116765397263259 0.8313779980816068 0.8125226272365237 0.8539072570361388 0.8083412267445644 0.991113247427112 0.9888888888888889 0.6925195368675767 0.6319444444444444 0.9916737965384765 0.9887645159937953 0.6790791210559541 0.6345885634588564 0.9920482283539964 0.988681618109411 0.670707767809217 0.6363636363636364 0.9901819171527819 0.9890962465925771 0.7176325969194958 0.6275862068965518 108.4 91 5364.6 4717 43.0 54 53.2 52
55 53 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9768008980206858 0.985958485958486 0.8305599513833293 0.8470544772514138 0.8695046414109691 0.8049361241017452 0.9015787132032951 0.7824831843905413 0.7824943622383087 0.953803733564405 0.9879734315034959 0.9928102532041263 0.6731464712631625 0.7012987012987013 0.9840389769345291 0.9962358845671268 0.7549703058874089 0.6136363636363636 0.981433632219028 0.9985328023475163 0.8217237941875621 0.5664335664335665 0.9946023322383573 0.9871529216742644 0.5703863922382604 0.9204545454545454 132.8 81 5307.2 4764 100.4 7 28.8 62
56 54 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9831213698039255 0.9792429792429792 0.8447223836724437 0.816333530229988 0.8369491326576923 0.816333530229988 0.8321801864351294 0.816333530229988 0.8595700811750235 0.816333530229988 0.9913175035335252 0.9893104171033327 0.698127263811362 0.6433566433566433 0.9919772121585618 0.9893104171033327 0.6819210531568227 0.6433566433566433 0.9924180371598071 0.9893104171033327 0.6719423357104516 0.6433566433566433 0.9902220513308638 0.9893104171033327 0.7289181110191832 0.6433566433566433 108.6 92 5366.6 4720 41.0 51 53.0 51
57 55 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9771599644479748 0.9861619861619861 0.8324678617345456 0.8499051328858289 0.870549413977389 0.8082749335304329 0.9017712959893579 0.7859796878870449 0.7851671959357567 0.9543529137800547 0.9881614220659258 0.9929137140475198 0.6767743014031652 0.7068965517241379 0.9843365833666231 0.9962775523861307 0.756762244588155 0.6202723146747352 0.9818034615439333 0.9985328023475163 0.8217391304347826 0.5734265734265734 0.9946041530837781 0.9873575129533678 0.5757302387877352 0.9213483146067416 132.8 82 5309.2 4764 98.4 7 28.8 61
58 56 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9843423439513362 0.9816849816849816 0.8515962672957779 0.834572685379137 0.8376462906674249 0.8304526291168367 0.8292008427523566 0.8277662392103808 0.8790989434369951 0.8416912547807393 0.9919525680686949 0.9905739421868454 0.7112399665228607 0.6785714285714286 0.9931182592966671 0.9909475713507397 0.6821743220381824 0.6699576868829337 0.9938974296961096 0.9911968140850974 0.6645042558086036 0.6643356643356644 0.9900178739811054 0.9899518526271719 0.7681800128928846 0.6934306569343066 107.4 95 5374.6 4729 33.0 42 54.2 48
59 57 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9852760585093231 0.9857549857549858 0.8690263252819322 0.844179493916305 0.8680626058102767 0.8015872466872441 0.8675359181449849 0.7789866808940379 0.8710806048279599 0.953244322524878 0.9924184181081515 0.9927068139195666 0.7456342324557129 0.6956521739130435 0.9924846474229035 0.9961942202333653 0.7436405641976499 0.6069802731411229 0.9925290865008431 0.9985328023475163 0.7425427497891267 0.5594405594405595 0.9923091821368931 0.9869484151646986 0.7498520275190266 0.9195402298850575 120.0 80 5367.2 4764 40.4 7 41.6 63
60 58 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9852760778521008 0.9806674806674807 0.8588289088157204 0.8247747610795253 0.8425367115336586 0.8201022404248155 0.8326871159186661 0.8170671290562299 0.8910285271242447 0.8328983330460691 0.9924350729673485 0.9900513142737459 0.7252227446640921 0.6594982078853047 0.9937770765750521 0.9904869667253373 0.6912963464922651 0.6497175141242938 0.9946741321517253 0.9907776147558164 0.6707000996856068 0.6433566433566433 0.9902078908716071 0.9893260778568439 0.791849163376882 0.6764705882352942 108.4 92 5378.8 4727 28.8 44 53.2 51
61 59 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9848450949778735 0.9851444851444852 0.864924748110117 0.8368088742868502 0.8637435478344082 0.794324313262103 0.863110952164852 0.7718888740687105 0.8675147138883881 0.9467568062272402 0.9921969158992823 0.9923950411501198 0.7376525803209517 0.6812227074235808 0.9922850182278722 0.9959434593509535 0.7352020774409445 0.5927051671732523 0.9923441342200501 0.9983232026828757 0.7338777701096542 0.5454545454545454 0.9920516034399549 0.9865368682684341 0.7429778243368215 0.9069767441860465 118.6 78 5366.2 4763 41.4 8 43.0 65
62 60 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9845578482836347 0.9812779812779813 0.8544638250488494 0.8320606603634808 0.8418456097943446 0.8293423228556376 0.8341174922949385 0.8275566395457403 0.878823416125884 0.8367004406945647 0.9920621690653177 0.9903624554787346 0.7168654810323813 0.6737588652482269 0.9931179952045985 0.9906115092837084 0.6905732243840907 0.6680731364275668 0.9938235130769559 0.9907776147558164 0.6744114715129208 0.6643356643356644 0.9903089597867266 0.9899476439790575 0.7673378724650413 0.6834532374100719 109.0 95 5374.2 4727 33.4 44 52.6 48
63 61 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9835522624118573 0.985958485958486 0.859819400342022 0.8483357077519362 0.8680366512201619 0.8077227180526358 0.8738491122676122 0.7858748880547246 0.8472910886146134 0.9492330016583748 0.9915194211436823 0.9928087545596664 0.728119379540362 0.703862660944206 0.9907932411435187 0.9961100886732475 0.7452800612968052 0.6193353474320241 0.9903099874368422 0.9983232026828757 0.757388237098382 0.5734265734265734 0.9927332020467304 0.9873548922056384 0.7018489751824966 0.9111111111111111 122.4 82 5355.2 4763 52.4 8 39.2 61
64 62 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9849169533964789 0.9812779812779813 0.8568370718580118 0.8285175879396984 0.8424930432630889 0.8217366302472686 0.8337112822546443 0.8173815285531907 0.8845398308062051 0.8405310494391176 0.9922484245557757 0.9903685092127303 0.721425719160248 0.6666666666666666 0.993436457225814 0.990990990990991 0.6915496293003638 0.6524822695035462 0.9942303246503819 0.991406413749738 0.6731922398589065 0.6433566433566433 0.9902757144574993 0.9893327755699645 0.7788039471549112 0.6917293233082706 108.8 92 5376.4 4730 31.2 41 52.8 51
65 63 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9838036669392924 0.9861619861619861 0.8604389160803467 0.8499051328858289 0.866624624741457 0.8082749335304329 0.8709884337468516 0.7859796878870449 0.8509751031181848 0.9543529137800547 0.9916519798387302 0.9929137140475198 0.7292258523219634 0.7068965517241379 0.9911127080258915 0.9962775523861307 0.7421365414570225 0.6202723146747352 0.9907538017778839 0.9985328023475163 0.7512230657158193 0.5734265734265734 0.9925532094702245 0.9873575129533678 0.7093969967661451 0.9213483146067416 121.4 82 5357.6 4764 50.0 7 40.2 61
66 64 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9851683385811363 0.9812779812779813 0.8591179961982995 0.8273020510383544 0.8445197440278023 0.8191718080178915 0.8356447838263932 0.8139898248890074 0.8877174472607932 0.8418923253954448 0.9923776876435134 0.990370525434373 0.7258583047530854 0.6642335766423357 0.993576841426931 0.99111744249382 0.6954626466286732 0.647226173541963 0.9943782878429562 0.9916160134143785 0.6769112798098305 0.6363636363636364 0.9903870939919786 0.9891281622412712 0.7850478005296078 0.6946564885496184 109.4 91 5377.2 4731 30.4 40 52.2 52
67 65 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9853837719899172 0.9863654863654864 0.8725280542968576 0.8502218435235476 0.8759525471290383 0.8060361396040803 0.8783811337989637 0.7826927840551818 0.8673464341461543 0.9645093543477004 0.9924692639993612 0.9930201062610688 0.752586844594354 0.7074235807860262 0.9921720610102851 0.9965707594513216 0.7597330332477916 0.6155015197568389 0.9919742912157481 0.9989520016767973 0.7647879763821793 0.5664335664335665 0.9929660693106076 0.9871582435791217 0.7417267989817009 0.9418604651162791 123.6 81 5364.2 4766 43.4 5 38.0 62
68 66 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9850964995053084 0.9831094831094831 0.8576525200541152 0.842521101783164 0.842063047060007 0.8318391286133222 0.832594650037817 0.8251081343724396 0.8882257267855923 0.8620684026326786 0.9923419932250359 0.9913152662969551 0.7229630468831946 0.6937269372693727 0.993629004874472 0.9922496857980729 0.6904970892455421 0.6714285714285714 0.994489200390027 0.9928736114022217 0.670700099685607 0.6573426573426573 0.9902056750746582 0.9897618052653573 0.7862457784965265 0.734375 108.4 94 5377.8 4737 29.8 34 53.2 49
69 67 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9852042000907175 0.9861619861619861 0.8730358853177561 0.8499051328858289 0.8803921292499022 0.8082749335304329 0.8854856737602657 0.7859796878870449 0.8614778517514337 0.9543529137800547 0.9923730027905566 0.9929137140475198 0.7536987678449553 0.7068965517241379 0.9917562570094823 0.9962775523861307 0.7690280014903218 0.6202723146747352 0.9913455519527066 0.9985328023475163 0.7796257955678245 0.5734265734265734 0.9934027519900305 0.9873575129533678 0.7295529515128367 0.9213483146067416 126.0 82 5360.8 4764 46.8 7 35.6 61
70 68 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9847014941973278 0.9812779812779813 0.8557353238012355 0.8297156201740179 0.842765198755447 0.824286573148787 0.8347978938030028 0.8207732322173739 0.8806089029482849 0.839213224523959 0.9921361681250811 0.9903664921465969 0.7193344794773899 0.6690647482014388 0.9932141454457468 0.9908645182919164 0.6923162520651469 0.6577086280056577 0.9939344119446302 0.9911968140850974 0.6756613756613756 0.6503496503496503 0.9903459676116533 0.989537560159029 0.7708718382849167 0.6888888888888889 109.2 93 5374.8 4729 32.8 42 52.4 50
71 69 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9842704855327307 0.9863654863654864 0.8671555742255741 0.8527317741939091 0.8776116210780414 0.8116037206067368 0.885004873750854 0.7894761913835484 0.8512211535369596 0.9548922056384743 0.9918874487567392 0.9930171964564878 0.7424236996944087 0.7124463519313304 0.9909847148856322 0.9963192236908148 0.7642385272704508 0.6268882175226587 0.9903839519338836 0.9985328023475163 0.7796257955678245 0.5804195804195804 0.993396261073106 0.9875621890547264 0.7090460460008134 0.9222222222222223 126.0 83 5355.6 4764 52.0 7 35.6 60
72 70 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9845219287457209 0.9808709808709809 0.8548607402256367 0.8235477478000577 0.8431495700912798 0.8155108731107743 0.835910688538176 0.8103885215601837 0.8770858837367401 0.8379709946007887 0.9920426007517154 0.9901611890307724 0.7176788796995579 0.656934306569343 0.9930215423583247 0.9909079482130138 0.6932775978242349 0.6401137980085349 0.9936755293652869 0.991406413749738 0.678145847711065 0.6293706293706294 0.9904164429838973 0.9889190884382187 0.7637553244895826 0.6870229007633588 109.6 90 5373.4 4730 34.2 41 52.0 53
73 71 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9852760262713604 0.9865689865689866 0.8752596174245599 0.8567291245529467 0.8847507536162851 0.8176664208104449 0.8915297682607773 0.796364398544235 0.860991885008248 0.9509738977992787 0.9924066586867115 0.9931192660550459 0.7581125761624083 0.7203389830508474 0.9915918132668311 0.9962350972599875 0.777909693965739 0.6390977443609023 0.991049721323334 0.9983232026828757 0.792009815198221 0.5944055944055944 0.99376931115138 0.9879693009749014 0.7282144588651158 0.9139784946236559 128.0 85 5359.2 4763 48.4 8 33.6 58
74 72 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9853838106754724 0.9833129833129833 0.8587022547988894 0.8415768557557877 0.8406382408501287 0.8272703303325926 0.8297448116169523 0.8184295268763934 0.8945555549776092 0.8686612601880559 0.9924922730441008 0.9914243882033047 0.7249122365536782 0.6917293233082706 0.9939773059542493 0.9926701570680628 0.6872991757460081 0.6618705035971223 0.9949700311780806 0.9935024103961434 0.6645195920558239 0.6433566433566433 0.9900279680622888 0.989355040701315 0.7990831418929297 0.7479674796747967 107.4 92 5380.4 4740 27.2 31 54.2 51
75 73 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9874667247809672 0.9865689865689866 0.8808944679765357 0.8530835228353733 0.8654268421316349 0.8093836088798947 0.8560356633611214 0.7861892875516854 0.911259487188576 0.9649457434116999 0.9935589253362499 0.9931235674098771 0.7682300106168213 0.7130434782608696 0.994759451265719 0.9966124377901384 0.736094232997551 0.622154779969651 0.9955618565895842 0.9989520016767973 0.7165094701326586 0.5734265734265734 0.9915662202419405 0.9873627511912161 0.8309527541352113 0.9425287356321839 115.8 82 5383.6 4766 24.0 5 45.8 61
76 74 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9856351700697598 0.9829059829059829 0.861594553268447 0.8377128766278801 0.8440630937039144 0.8235684959885494 0.833470833667023 0.8148282235475697 0.896280376162564 0.8644918571915838 0.9926206592873459 0.9912152269399708 0.7305684472495483 0.6842105263157895 0.9940509272401421 0.9924607329842932 0.6940752601676868 0.6546762589928058 0.9950069997472047 0.9932928107315029 0.6719346675868414 0.6363636363636364 0.9902470619918973 0.9891463160091839 0.8023136903332307 0.7398373983739838 108.6 91 5380.6 4739 27.0 32 53.0 52
77 75 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9876821388469705 0.9863654863654864 0.8824481257185894 0.8527317741939091 0.8657090989204775 0.8116037206067368 0.8555593092061944 0.7894761913835484 0.9153499265289049 0.9548922056384743 0.9936704416866011 0.9930171964564878 0.7712258097505776 0.7124463519313304 0.9949592647185364 0.9963192236908148 0.7364589331224185 0.6268882175226587 0.9958207118101343 0.9985328023475163 0.7152979066022545 0.5804195804195804 0.9915312796956883 0.9875621890547264 0.8391685733621216 0.9222222222222223 115.6 83 5385.0 4764 22.6 7 46.0 60
78 76 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9851324190432222 0.9833129833129833 0.8584062804905994 0.84385854781335 0.8434541223782931 0.8324033525694168 0.8344095162212989 0.8252129342047598 0.8879972785848785 0.8649607121650007 0.9923597171974384 0.9914207993304038 0.7244528437837602 0.6962962962962963 0.9935917185938852 0.992417577814084 0.693316526162701 0.6723891273247496 0.9944152290532873 0.9930832110668623 0.6744038033893106 0.6573426573426573 0.990315033423508 0.9897639440150408 0.7856795237462493 0.7401574803149606 109.0 94 5377.4 4738 30.2 33 52.6 49
79 77 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9870357225639627 0.9865689865689866 0.8798050558534396 0.8567291245529467 0.8698662779881701 0.8176664208104449 0.8636401794756614 0.796364398544235 0.8982100252815538 0.9509738977992787 0.9933328198400833 0.9931192660550459 0.7662772918667957 0.7203389830508474 0.9941148806201131 0.9962350972599875 0.745617675356227 0.6390977443609023 0.9946371567429029 0.9983232026828757 0.7326432022084196 0.5944055944055944 0.9920329803869183 0.9879693009749014 0.8043870701761892 0.9139784946236559 118.4 85 5378.6 4763 29.0 8 43.2 58
80 78 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.985527475931943 0.9824989824989825 0.861442572251889 0.8385649607532444 0.8451410509401756 0.830154612739243 0.8352270579212874 0.8247937348754787 0.893287438450975 0.8536563177794128 0.9925640208925245 0.9909985346451748 0.7303211236112532 0.6861313868613139 0.9938953165563065 0.9917459253362383 0.6963867853240447 0.6685633001422475 0.9947850720575893 0.9922448124083001 0.6756690437849857 0.6573426573426573 0.9903540470495413 0.9897553836504286 0.7962208298524088 0.7175572519083969 109.2 94 5379.4 4734 28.2 37 52.4 49
81 79 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.05, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9869280026357758 0.9865689865689866 0.8787019179213326 0.8530835228353733 0.8682252121041018 0.8093836088798947 0.8617730118956839 0.7861892875516854 0.8986740607495708 0.9649457434116999 0.993277425306436 0.9931235674098771 0.7641264105362291 0.7130434782608696 0.9940925687027711 0.9966124377901384 0.7423578555054322 0.622154779969651 0.9946371977810925 0.9989520016767973 0.7289088260102753 0.5734265734265734 0.9919236322843235 0.9873627511912161 0.8054244892148184 0.9425287356321839 117.8 82 5378.6 4766 29.0 5 43.8 61
82 80 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9863534576665582 0.9831094831094831 0.8674259262522324 0.8402287382378553 0.8479481142920413 0.8267109219956497 0.8362511979033694 0.8183247270440731 0.9064151642130923 0.8656441511212876 0.992991297454395 0.9913188996966844 0.74186055505007 0.6891385767790262 0.9945540101913807 0.9925023037614141 0.7013422183927017 0.6609195402298851 0.9955987841205187 0.9932928107315029 0.6769036116862204 0.6433566433566433 0.9903985652830037 0.9893528183716075 0.8224317631431811 0.7419354838709677 109.4 92 5383.8 4739 23.8 32 52.2 51
83 81 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9871075487446055 0.9867724867724867 0.8790069366852771 0.8583142406586364 0.866173804471608 0.818231408861414 0.8582459063246997 0.7964691983765553 0.9033657949355869 0.9559424197067787 0.9933720613553818 0.9932242259981237 0.7646418120151725 0.723404255319149 0.9943744210297952 0.9964025767589726 0.7379731879134208 0.6400602409638554 0.9950440709118029 0.9985328023475163 0.7214477417375968 0.5944055944055944 0.9917070764292699 0.9879717959352966 0.8150245134419036 0.9239130434782609 116.6 85 5380.8 4764 26.8 7 45.0 58
84 82 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9862815928003602 0.9837199837199837 0.8672655258524721 0.8443024980038782 0.848994568796488 0.8283932426412637 0.8380105701949757 0.8186391265410339 0.9038070075160807 0.8748450305455787 0.9929535279033799 0.9916352990380594 0.7415775238015643 0.696969696969697 0.9944280443607486 0.9930058215018637 0.7035610932322273 0.6637806637806638 0.9954138250000273 0.9939216097254244 0.6806073153899241 0.6433566433566433 0.9905070103243874 0.9893594825787607 0.8171070047077738 0.7603305785123967 110.0 92 5382.8 4742 24.8 29 51.6 51
85 83 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9878616914033926 0.9863654863654864 0.8882433872015938 0.8539546788327481 0.8792732315001988 0.8143617723634435 0.8736615139018262 0.7928678950477315 0.9048913645687214 0.9504039456837321 0.993756270186615 0.9930157406442197 0.7827305042165724 0.7148936170212766 0.9944396638423738 0.9961934242449594 0.7641067991580239 0.6325301204819277 0.9948960666810391 0.9983232026828757 0.7524269611226133 0.5874125874125874 0.9926204964822525 0.9877644131065948 0.8171622326551903 0.9130434782608695 121.6 84 5380.0 4763 27.6 8 40.0 59
86 84 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9859943138681591 0.9829059829059829 0.8649774102836663 0.8411930908822298 0.8473115871981978 0.8312762678885206 0.8366573908444179 0.8250033345401193 0.9001315205110121 0.859220918082185 0.9928052425916853 0.9912097111762244 0.7371495779756474 0.6911764705882353 0.9942356983744481 0.9920817797142737 0.7003874760219477 0.6704707560627675 0.9951919383486014 0.9926640117375812 0.6781228433402346 0.6573426573426573 0.9904314967884258 0.9897596656217346 0.8098315442335986 0.7286821705426356 109.6 94 5381.6 4736 26.0 35 52.0 49
87 85 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9867843889600453 0.9873829873829874 0.8785119111214078 0.8642901813633521 0.8700226297866667 0.8226731525839097 0.8647044966165968 0.8001753015376993 0.8942347767857424 0.9666182873730043 0.9932018270737839 0.9935376276839691 0.7638219951690315 0.7350427350427351 0.9938628685047399 0.996779186012465 0.7461823910685934 0.6485671191553545 0.9943043233459147 0.9989520016767973 0.7351046698872786 0.6013986013986014 0.9921031218224202 0.9881816296910636 0.7963664317490646 0.945054945054945 118.8 86 5376.8 4766 30.8 5 42.8 57
88 86 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9853838106754724 0.9816849816849816 0.8608320644828911 0.8286252354048964 0.8460195719166173 0.8176782825713964 0.8369418071995559 0.8108077208894647 0.889377571584087 0.84879488246547 0.9924890562531894 0.9905838041431262 0.7291750727125927 0.6666666666666666 0.9937102146304163 0.9915797411084579 0.6983289292028184 0.6437768240343348 0.9945262031576426 0.9922448124083001 0.6793574112414691 0.6293706293706294 0.9904612858624674 0.9889283476081053 0.7882938573057066 0.7086614173228346 109.8 90 5378.0 4734 29.6 37 51.8 53
89 87 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.987323053076904 0.986975986975987 0.884598514469672 0.8610706662331604 0.8787856640754045 0.8215303999383525 0.8751728471338343 0.7999657018730588 0.8954908545017657 0.9564539548079303 0.9934769475740686 0.9933277731442869 0.775720081365275 0.7288135593220338 0.993928642865467 0.9964442585233215 0.7636426852853422 0.6466165413533834 0.9942303656885716 0.9985328023475163 0.7561153285790967 0.6013986013986014 0.9927264837778942 0.9881767268201618 0.7982552252256373 0.9247311827956989 122.2 86 5376.4 4764 31.2 7 39.4 57
90 88 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'robust_scaling'} 0.9862816121431379 0.9839234839234839 0.8660646344104338 0.8433624344377819 0.8458825049631292 0.8237478893318506 0.8338075284846095 0.8119605190449877 0.9067069512881453 0.8824078998433256 0.9929553768455538 0.9917441738948688 0.7391738919753138 0.694980694980695 0.9945838653873544 0.9934260112218407 0.6971811445389042 0.6540697674418605 0.995672721258767 0.9945504087193461 0.6719423357104516 0.6293706293706294 0.9902539371851116 0.9889537307211338 0.8231599653911792 0.7758620689655172 108.6 90 5384.2 4745 23.4 26 53.0 53
91 89 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9878976302840842 0.9857549857549858 0.8820709100771731 0.8454905779707063 0.8606902149712283 0.8043882221350002 0.8478629619375315 0.7823783845582211 0.9248508322510147 0.9486313093089597 0.9937849687886153 0.9927052938724469 0.7703568513657307 0.6982758620689655 0.9954261494332506 0.9960684261156887 0.7259542805092061 0.6127080181543116 0.9965234087305191 0.9983232026828757 0.6992025151445442 0.5664335664335665 0.9910621488085394 0.9871502590673575 0.8586395156934901 0.9101123595505618 113.0 81 5388.8 4763 18.8 8 48.6 62
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93 91 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'robust_scaling'} 0.9877180777276621 0.9863654863654864 0.8816788103460759 0.8502218435235476 0.8635452070705993 0.8060361396040803 0.8525685204705444 0.7826927840551818 0.9174187901108943 0.9645093543477004 0.9936906624647277 0.9930201062610688 0.7696669582274238 0.7074235807860262 0.99510035731344 0.9965707594513216 0.7319900568277584 0.6155015197568389 0.9960426463394482 0.9989520016767973 0.709094394601641 0.5664335664335665 0.9913510840375738 0.9871582435791217 0.8434864961842148 0.9418604651162791 114.6 81 5386.2 4766 21.4 5 47.0 62
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98 96 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'minmax_scaling'} 0.9849529696482806 0.9837199837199837 0.8594042495202323 0.8454408348836953 0.8483534317802419 0.8309721646766357 0.841560102542602 0.8220308302052171 0.8805834053942547 0.872830663184528 0.9922633264979815 0.9916335494666387 0.7265451725424832 0.6992481203007519 0.9931764562837204 0.9928795811518325 0.7035304072767634 0.6690647482014388 0.9937865650269266 0.9937120100607839 0.6893336400582777 0.6503496503496503 0.9907468388274416 0.989563765393446 0.7704199719610678 0.7560975609756098 111.4 93 5374.0 4741 33.6 30 50.2 50
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102 100 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'minmax_scaling'} 0.984306559812865 0.9827024827024827 0.8559248759596414 0.836378828315876 0.8487277440374303 0.8230142515447478 0.8442212127583039 0.8147234237152493 0.8692349807209588 0.8615075089231599 0.9919268868035575 0.9911097165568455 0.7199228651157252 0.6816479400749064 0.9925318995838441 0.9922928709055877 0.7049235884910162 0.6537356321839081 0.9929359459521571 0.9930832110668623 0.6955064795644507 0.6363636363636364 0.9909213407706596 0.9891440501043841 0.7475486206712582 0.7338709677419355 112.4 91 5369.4 4738 38.2 33 49.2 52
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105 103 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'minmax_scaling'} 0.9773754816754584 0.9857549857549858 0.8338088988641548 0.8454905779707063 0.8719046413641168 0.8043882221350002 0.9030798489067287 0.7823783845582211 0.786403231099035 0.9486313093089597 0.9882738719118173 0.9927052938724469 0.6793439258164924 0.6982758620689655 0.9844705208627319 0.9960684261156887 0.7593387618655016 0.6127080181543116 0.9819514315762057 0.9983232026828757 0.8242082662372517 0.5664335664335665 0.9946795847839335 0.9871502590673575 0.5781268774141367 0.9101123595505618 133.2 81 5310.0 4763 97.6 8 28.4 62
106 104 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'minmax_scaling'} 0.9855275533030532 0.9837199837199837 0.8620473152027145 0.8443024980038782 0.8469072502205531 0.8283932426412637 0.837652932499798 0.8186391265410339 0.8913633732693429 0.8748450305455787 0.992563146348559 0.9916352990380594 0.7315314840568701 0.696969696969697 0.9938063944856144 0.9930058215018637 0.7000081059554918 0.6637806637806638 0.9946372046207905 0.9939216097254244 0.6806686603788054 0.6433566433566433 0.9904989369385204 0.9893594825787607 0.7922278096001655 0.7603305785123967 110.0 92 5378.6 4742 29.0 29 51.6 51
107 105 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'minmax_scaling'} 0.9847014426165878 0.9863654863654864 0.8632079102851252 0.8527317741939091 0.8612717703342152 0.8116037206067368 0.8600276838689567 0.7894761913835484 0.86662453445884 0.9548922056384743 0.9921239280877264 0.9930171964564878 0.7342918924825239 0.7124463519313304 0.9922781686451534 0.9963192236908148 0.730265372023277 0.6268882175226587 0.9923811096288725 0.9985328023475163 0.7276742581090407 0.5804195804195804 0.9918673160893929 0.9875621890547264 0.7413817528282872 0.9222222222222223 117.6 83 5366.4 4764 41.2 7 44.0 60
108 106 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'minmax_scaling'} 0.985168448190209 0.9849409849409849 0.8593919425146102 0.8549107605977276 0.8454002284319145 0.8369669345371191 0.836873048813246 0.8260513328633219 0.886667818898389 0.8898484941421825 0.9923773235653067 0.9922642692870584 0.7264065614639134 0.7175572519083969 0.9935323970335972 0.9937602077138908 0.697268059830232 0.6801736613603473 0.9943043370253113 0.9947600083839866 0.6794417606011809 0.6573426573426573 0.9904597230352182 0.9897810218978103 0.7828759147615595 0.7899159663865546 109.8 94 5376.8 4746 30.8 25 51.8 49
109 107 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'minmax_scaling'} 0.9851683450287287 0.9853479853479854 0.867078731383568 0.8383552631578948 0.8647195853985246 0.7948591039779475 0.8632888847281579 0.7719936739010309 0.871587954768321 0.9520933575335291 0.9923646861628213 0.9925 0.741792776604315 0.6842105263157895 0.9925518659028729 0.9961109020198219 0.7368873048941765 0.593607305936073 0.9926769949758313 0.9985328023475163 0.7339007744804846 0.5454545454545454 0.9920540899148221 0.9865396562435287 0.7511218196218196 0.9176470588235294 118.6 78 5368.0 4764 39.6 7 43.0 65
110 108 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'minmax_scaling'} 0.9850607089192449 0.9839234839234839 0.8607782809562249 0.8456802466215746 0.8504002566461626 0.8289567585128061 0.8440145778627667 0.8187439263733541 0.8806303642311141 0.8780141843971632 0.9923181157504943 0.9917407213800313 0.7292384461619557 0.6996197718631179 0.9931762343075448 0.9931736326325488 0.7076242789847803 0.6647398843930635 0.9937495759387076 0.9941312093900649 0.6942795797868262 0.6433566433566433 0.9908929438821478 0.9893617021276596 0.7703677845800805 0.7666666666666667 112.2 92 5373.8 4743 33.8 28 49.4 51
111 109 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.03, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'minmax_scaling'} 0.983911373972294 0.985958485958486 0.8611094302735921 0.8470544772514138 0.8665800232613059 0.8049361241017452 0.8704297780460554 0.7824831843905413 0.852701789714932 0.953803733564405 0.9917079857709886 0.9928102532041263 0.7305108747761956 0.7012987012987013 0.9912239163984287 0.9962358845671268 0.7419361301241829 0.6136363636363636 0.9909017307719667 0.9985328023475163 0.7499578253201442 0.5664335664335665 0.9925168864014176 0.9871529216742644 0.7128866930284468 0.9204545454545454 121.2 81 5358.4 4764 49.2 7 40.4 62
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180 178 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'yeo_johnson'} 0.9863533996382257 0.9837199837199837 0.8665158446879632 0.8465621682383064 0.8449142609616143 0.8335359137703875 0.8320634890203745 0.8254225338694003 0.9104443418129394 0.8708841094174149 0.9929926205074378 0.9916317991631799 0.7400390688684887 0.7014925373134329 0.9947095380588344 0.9927533196498136 0.6951189838643943 0.6743185078909613 0.9958576735395603 0.9935024103961434 0.6682693045011886 0.6573426573426573 0.9901450962124443 0.9897682188348298 0.8307435874134347 0.752 108.0 94 5385.2 4740 22.4 31 53.6 49
181 179 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'yeo_johnson'} 0.9875026185285112 0.9867724867724867 0.884330042257524 0.8594806048391886 0.8739490439923376 0.8209612097036796 0.8674733757667736 0.7998609020407386 0.9036557484611804 0.9515339454400988 0.9935725700367068 0.9932228130539047 0.7750875144783408 0.7257383966244726 0.9943661221679145 0.9962767737617135 0.7535319658167605 0.6456456456456456 0.9948961419177202 0.9983232026828757 0.740050609615827 0.6013986013986014 0.9922539090044488 0.9881742738589212 0.8150575879179118 0.9148936170212766 119.6 86 5380.0 4763 27.6 8 42.0 57
182 180 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'yeo_johnson'} 0.9861379597818521 0.9835164835164835 0.866771900309337 0.8429347386448162 0.8487748575603067 0.8278311019035429 0.8379558067856019 0.8185343267087136 0.9026699517361976 0.8717278113796217 0.9928783776685262 0.9915298546481229 0.7406654229501483 0.6943396226415094 0.9943093143291806 0.9928379963142905 0.703240400791433 0.6628242074927954 0.9952659575632289 0.9937120100607839 0.6806456560079749 0.6433566433566433 0.9905041612576205 0.9893572621035058 0.8148357422147745 0.7540983606557377 110.0 92 5382.0 4741 25.6 30 51.6 51
183 181 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'yeo_johnson'} 0.9868561700075403 0.986975986975987 0.8796188911045947 0.8622094093111696 0.8727352610031494 0.8242461833970267 0.8683380596255166 0.8033574055372421 0.8919377564270367 0.9520844027478949 0.9932381160991426 0.9933263816475495 0.7659996661100468 0.7310924369747899 0.9937888059886074 0.9963184537505753 0.7516817160176913 0.6521739130434783 0.9941563738327371 0.9983232026828757 0.7425197454182961 0.6083916083916084 0.9923220597598086 0.9883793318115792 0.791553453094265 0.9157894736842105 120.0 87 5376.0 4763 31.6 8 41.6 56
184 182 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'yeo_johnson'} 0.9855634148126345 0.9824989824989825 0.8637331011735896 0.8326251853541691 0.8493780719867259 0.8172566220059624 0.8406768865214092 0.8078352165545626 0.8919055092649695 0.8621353799359603 0.9925793029523307 0.9910079464659138 0.7348868993948485 0.6742424242424242 0.993724089672108 0.9923776018762827 0.7050320543013436 0.6421356421356421 0.9944892687870096 0.9932928107315029 0.6868645042558086 0.6223776223776224 0.9906792343558453 0.9887335697892761 0.7931317841740936 0.7355371900826446 111.0 89 5377.8 4739 29.8 32 50.6 54
185 183 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 6, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'yeo_johnson'} 0.9869280155309609 0.985958485958486 0.8826676504459643 0.8470544772514138 0.8789456300777816 0.8049361241017452 0.8767811321344683 0.7824831843905413 0.890261538812334 0.953803733564405 0.9932706480038191 0.9928102532041263 0.7720646528881092 0.7012987012987013 0.9935353631066063 0.9962358845671268 0.7643558970489572 0.6136363636363636 0.9937125594916955 0.9985328023475163 0.7598497047772412 0.5664335664335665 0.9928323286968121 0.9871529216742644 0.7876907489278562 0.9204545454545454 122.8 81 5373.6 4764 34.0 7 38.8 62
186 184 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'yeo_johnson'} 0.9869639415164674 0.9845339845339846 0.8726840371150519 0.8487424363927973 0.851149342600672 0.828051489000142 0.838388794706144 0.8156666222061317 0.9168367617602247 0.8902343785390072 0.9933057246808102 0.9920585161964472 0.7520623495492934 0.7054263565891473 0.9949901004453066 0.9938031235606917 0.7073085847560374 0.6622998544395924 0.9961165971570931 0.9949696080486271 0.6806609922551952 0.6363636363636364 0.9905126540199793 0.9891644092519275 0.8431608695004702 0.7913043478260869 110.0 91 5386.6 4747 21.0 24 51.6 52
187 185 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'yeo_johnson'} 0.9876103642470679 0.9861619861619861 0.8788810270598635 0.8499051328858289 0.8571088017964599 0.8082749335304329 0.8441222794051564 0.7859796878870449 0.9229738792894443 0.9543529137800547 0.9936379645982708 0.9929137140475198 0.7641240895214564 0.7068965517241379 0.9953228213453992 0.9962775523861307 0.7188947822475205 0.6202723146747352 0.9964494510731761 0.9985328023475163 0.6917951077371367 0.5734265734265734 0.9908433514173332 0.9873575129533678 0.8551044071615552 0.9213483146067416 111.8 82 5388.4 4764 19.2 7 49.8 61
188 186 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'yeo_johnson'} 0.9861379726770372 0.9841269841269841 0.8655409029545755 0.8459239130434782 0.8456436922034275 0.8269224843623216 0.8337565922973423 0.8154570225414912 0.9057043723562375 0.8834688346883469 0.992880278067109 0.9918478260869565 0.7382015278420421 0.7 0.9944651338532046 0.9934676102340773 0.6968222505536504 0.660377358490566 0.995524840142572 0.9945504087193461 0.6719883444521126 0.6363636363636364 0.990251316203827 0.989159891598916 0.8211574285086479 0.7777777777777778 108.6 91 5383.4 4745 24.2 26 53.0 52
189 187 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 1, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'yeo_johnson'} 0.9876103255615127 0.9867724867724867 0.8791891827878047 0.8559208843672739 0.8579827803715039 0.8127209992015513 0.8453160241563669 0.7896857910481889 0.9220405870309436 0.965374580868779 0.9936374673126374 0.9932270501198291 0.7647408982629722 0.7186147186147186 0.995278311289981 0.9966541196152238 0.7206872494530266 0.6287878787878788 0.996375472896738 0.9989520016767973 0.6942565754159957 0.5804195804195804 0.9909157071099024 0.9875673435557397 0.8531654669519847 0.9431818181818182 112.2 83 5388.0 4766 19.6 5 49.4 60
190 188 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'yeo_johnson'} 0.9860302656440355 0.9827024827024827 0.8649460777272889 0.8351784294420911 0.8461713033990161 0.8204170756400867 0.834914033984797 0.8113317200510661 0.9026337700286632 0.863322408932921 0.9928242792239658 0.9911115758653143 0.7370678762306121 0.6792452830188679 0.9943319241497942 0.9924191656893953 0.6980106826482381 0.6484149855907781 0.995339915220572 0.9932928107315029 0.6744881527490223 0.6293706293706294 0.9903230433373681 0.9889398998330551 0.8149444967199582 0.7377049180327869 109.0 90 5382.4 4739 25.2 32 52.6 53
191 189 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 0.8, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'yeo_johnson'} 0.9869280219785533 0.9865689865689866 0.8763001326976253 0.8555347091932457 0.8613527103869986 0.814922530688772 0.8521808415878034 0.7929726948800518 0.9050037909455038 0.955421936554012 0.9932814075310376 0.9931207004377736 0.7593188578642132 0.717948717948718 0.9944488685799555 0.9963608984816162 0.7282565521940416 0.6334841628959276 0.995228947955915 0.9985328023475163 0.7091327352196918 0.5874125874125874 0.9913426930080359 0.9877669500311009 0.8186648888829717 0.9230769230769231 114.6 84 5381.8 4764 25.8 7 47.0 59
192 190 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'smote_k_neighbors': 10, 'smote_n_clusters': 5, 'sampling_method': 'KMeansSMOTE', 'scaling_method': 'yeo_johnson'} 0.9858866132827497 0.9835164835164835 0.8650030578546822 0.8452055343239073 0.8484471242242065 0.8329689456470806 0.838432819606199 0.8253177340370801 0.8976549194888044 0.8678989139515456 0.9927482957618589 0.9915263102835025 0.7372578199475056 0.6988847583643123 0.9940799505450167 0.9925854557640751 0.7028142979033961 0.673352435530086 0.9949700790559686 0.9932928107315029 0.6818955601564297 0.6573426573426573 0.9905382524089305 0.9897660818713451 0.8047715865686783 0.746031746031746 110.2 94 5380.4 4739 27.2 32 51.4 49
193 191 CatBoostClassifier {'iterations': 500, 'learning_rate': 0.1, 'depth': 8, 'l2_leaf_reg': 3, 'subsample': 1.0, 'loss_function': 'Logloss', 'verbose': False, 'random_seed': 42, 'sampling_method': 'class_weight', 'scaling_method': 'yeo_johnson'} 0.9867124918558847 0.9863654863654864 0.8763060935692055 0.8502218435235476 0.8645769967271869 0.8060361396040803 0.8574628643412587 0.7826927840551818 0.8992122472214948 0.9645093543477004 0.9931670881582635 0.9930201062610688 0.7594450989801474 0.7074235807860262 0.994070409898111 0.9965707594513216 0.7350835835562627 0.6155015197568389 0.9946742142281044 0.9989520016767973 0.7202515144544129 0.5664335664335665 0.9916678816816621 0.9871582435791217 0.8067566127613276 0.9418604651162791 116.4 81 5378.8 4766 28.8 5 45.2 62

0
results_lgbm_tuning.csv → results/results_lgbm_tuning.csv
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Can't render this file because it is too large.

45
utils.py
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@@ -99,3 +99,48 @@ def scaling_handler(data_frame, method="robust_scaling"):
data_frame_scaled["label"] = labels.values
return data_frame_scaled
from sklearn.metrics import (
accuracy_score,
f1_score,
fbeta_score,
precision_score,
recall_score,
)
def get_metrics(y_true, y_pred, prefix=""):
metrics = {}
metrics[f"{prefix}accuracy"] = accuracy_score(y_true, y_pred)
metrics[f"{prefix}f1_macro"] = f1_score(y_true, y_pred, average="macro")
metrics[f"{prefix}f2_macro"] = fbeta_score(y_true, y_pred, beta=2, average="macro")
metrics[f"{prefix}recall_macro"] = recall_score(y_true, y_pred, average="macro")
metrics[f"{prefix}precision_macro"] = precision_score(
y_true, y_pred, average="macro"
)
# Per-class scores
f1_scores = f1_score(y_true, y_pred, average=None, zero_division=0)
f2_scores = fbeta_score(y_true, y_pred, beta=2, average=None, zero_division=0)
recall_scores = recall_score(y_true, y_pred, average=None, zero_division=0)
precision_scores = precision_score(y_true, y_pred, average=None, zero_division=0)
for i in range(len(f1_scores)):
metrics[f"{prefix}f1_class{i}"] = f1_scores[i]
metrics[f"{prefix}f2_class{i}"] = f2_scores[i]
metrics[f"{prefix}recall_class{i}"] = recall_scores[i]
metrics[f"{prefix}precision_class{i}"] = precision_scores[i]
# Confusion-matrix components
TP = sum((y_true == 1) & (y_pred == 1))
TN = sum((y_true == 0) & (y_pred == 0))
FP = sum((y_true == 0) & (y_pred == 1))
FN = sum((y_true == 1) & (y_pred == 0))
metrics[f"{prefix}TP"] = TP
metrics[f"{prefix}TN"] = TN
metrics[f"{prefix}FP"] = FP
metrics[f"{prefix}FN"] = FN
return metrics