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@@ -91,21 +91,21 @@ We are dealing with an exteremly imbalance dataset related to electrocardiogram
## STEP 5:
Current results taken:
Current results taken KMEANS_SMOTE:
| model | stage | accuracy | f1_macro | f2_macro | recall_macro | precision_macro | f1_class0 | f1_class1 | f2_class0 | f2_class1 | recall_class0 | recall_class1 | precision_class0 | precision_class1 | TP | TN | FP | FN |
|-----------------------|-------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|-----|-------|----|----|
| CatBoost_balanced | train | 0.9843784049402589 | 0.8696686267343388 | 0.8824472728294012 | 0.8916952848998795 | 0.8508242781484853 | 0.9919396338322237 | 0.7473976196364541 | 0.9908276010500254 | 0.7740669446087769 | 0.9900881006639566 | 0.7933024691358025 | 0.9938004847319636 | 0.7078480715650071 | 789 | 26898 | 140 | 19 |
| CatBoost_balanced | test | 0.9802604802604803 | 0.8348421298822796 | 0.8461546793313885 | 0.8541662696976049 | 0.8176680164072361 | 0.9898162729658793 | 0.6798679867986799 | 0.988757446094471 | 0.703551912568306 | 0.9880528191154894 | 0.7202797202797203 | 0.991586032814472 | 0.64375 | 103 | 4714 | 57 | 40 |
| LGBM_KMEANS_SMOTE | train | 0.9883286128479746 | 0.8784419356817057 | 0.8436008106620193 | 0.8240767336379762 | 0.9582821430574249 | 0.9940169232360254 | 0.7628669481273861 | 0.9966698960611392 | 0.6905317252628993 | 0.9984466771524954 | 0.6497067901234568 | 0.9896275269971563 | 0.9269367591176938 | 775 | 27036 | 2 | 33 |
| LGBM_KMEANS_SMOTE | 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 |
| CatBoost_balanced_knn10 | train | 0.9843784049402589 | 0.8696686267343388 | 0.8824472728294012 | 0.8916952848998795 | 0.8508242781484853 | 0.9919396338322237 | 0.7473976196364541 | 0.9908276010500254 | 0.7740669446087769 | 0.9900881006639566 | 0.7933024691358025 | 0.9938004847319636 | 0.7078480715650071 | 789 | 26898 | 140 | 19 |
| CatBoost_balanced_knn10 | test | 0.9802604802604803 | 0.8348421298822796 | 0.8461546793313885 | 0.8541662696976049 | 0.8176680164072361 | 0.9898162729658793 | 0.6798679867986799 | 0.988757446094471 | 0.703551912568306 | 0.9880528191154894 | 0.7202797202797203 | 0.991586032814472 | 0.64375 | 103 | 4714 | 57 | 40 |
| LGBM_KMEANS_SMOTE_knn10 | train | 0.9883286128479746 | 0.8784419356817057 | 0.8436008106620193 | 0.8240767336379762 | 0.9582821430574249 | 0.9940169232360254 | 0.7628669481273861 | 0.9966698960611392 | 0.6905317252628993 | 0.9984466771524954 | 0.6497067901234568 | 0.9896275269971563 | 0.9269367591176938 | 775 | 27036 | 2 | 33 |
| 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 |
## next steps:
```
✅ 1. Stratified K-fold only apply on train.
🗹 2. train LGBM model using KMEANS_SMOTE with k_neighbors=10 (fine-tune remained)
🗹 3. train Cat_boost using KMEANS_SMOTE with k_neighbors=10 (fine-tune remained)
🗹 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)
🗹 4. implement proposed methods of this article : https://1drv.ms/b/c/ab2a38fe5c318317/IQBEDsSFcYj6R6AMtOnh0X6DAZUlFqAYq19WT8nTeXomFwg
🗹 5. compare proposed model with SMOTE vs oversampling balancing method
```