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gradient_boosting_balanced_report.txt
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75 lines (63 loc) · 5.36 KB
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================================================================================
РЕЗУЛЬТАТЫ МОДЕЛЕЙ С БАЛАНСИРОВКОЙ КЛАССОВ
================================================================================
Дата анализа: 2025-12-28 12:35:35
================================================================================
ПРОБЛЕМА ДИСБАЛАНСА
================================================================================
Распределение классов:
Класс 0 (снижение): 87 (35.2%)
Класс 1 (рост): 160 (64.8%)
Методы балансировки:
- XGBoost: scale_pos_weight=0.544 + sample_weight
- LightGBM: class_weight='balanced' + sample_weight
- CatBoost: auto_class_weights='Balanced'
================================================================================
СТРУКТУРА ФОЛДОВ
================================================================================
Fold Train_Start Train_End Test_Start Test_End Train_Size Test_Size Train_Class_0 Train_Class_1 Test_Class_0 Test_Class_1
0 1 2005-01-31 2009-12-31 2010-05-31 2012-06-30 60 26 16 44 11 15
1 2 2005-01-31 2012-02-29 2012-07-31 2014-08-31 86 26 27 59 13 13
2 3 2005-01-31 2014-04-30 2014-09-30 2016-10-31 112 26 40 72 5 21
3 4 2005-01-31 2016-06-30 2016-11-30 2018-12-31 138 26 47 91 7 19
4 5 2005-01-31 2018-08-31 2019-01-31 2021-02-28 164 26 53 111 6 20
5 6 2005-01-31 2020-10-31 2021-03-31 2023-04-30 190 26 60 130 12 14
6 7 2005-01-31 2022-12-31 2023-05-31 2025-06-30 216 26 72 144 14 12
================================================================================
СРАВНЕНИЕ: BALANCED vs UNBALANCED
================================================================================
Model Accuracy_Mean Recall_0_Mean Recall_1_Mean F1_Macro_Mean ROC_AUC_Mean
0 XGBoost_balanced 0.532967 0.329944 0.671037 0.475392 0.549240
1 XGBoost_unbalanced 0.609890 0.229604 0.835303 0.500463 0.538674
2 LightGBM_balanced 0.532967 0.422392 0.609846 0.495066 0.540586
3 LightGBM_unbalanced 0.587912 0.227737 0.798460 0.476754 0.538919
4 CatBoost_balanced 0.604396 0.277544 0.809040 0.510276 0.595839
5 CatBoost_unbalanced 0.609890 0.220173 0.849893 0.487860 0.613771
================================================================================
ПОЛНЫЕ РЕЗУЛЬТАТЫ
================================================================================
Model Balanced Accuracy_Mean Accuracy_Std Precision_0_Mean Recall_0_Mean F1_0_Mean Precision_1_Mean Recall_1_Mean F1_1_Mean F1_Macro_Mean F1_Macro_Std ROC_AUC_Mean ROC_AUC_Std Log_Loss_Mean
0 XGBoost_balanced True 0.532967 0.097363 0.397817 0.329944 0.319214 0.641897 0.671037 0.631571 0.475392 0.118134 0.549240 0.138614 0.861700
1 XGBoost_unbalanced True 0.609890 0.115254 0.509524 0.229604 0.283247 0.647859 0.835303 0.717680 0.500463 0.120542 0.538674 0.145536 0.918661
2 LightGBM_balanced True 0.532967 0.092921 0.450578 0.422392 0.381711 0.660792 0.609846 0.608420 0.495066 0.110473 0.540586 0.133677 0.923207
3 LightGBM_unbalanced True 0.587912 0.102204 0.344780 0.227737 0.256504 0.640350 0.798460 0.697005 0.476754 0.103117 0.538919 0.103957 0.916153
4 CatBoost_balanced True 0.604396 0.100115 0.549145 0.277544 0.311654 0.659128 0.809040 0.708898 0.510276 0.117166 0.595839 0.156904 0.890517
5 CatBoost_unbalanced True 0.609890 0.117073 0.539508 0.220173 0.256471 0.650699 0.849893 0.719249 0.487860 0.127080 0.613771 0.150872 0.939577
================================================================================
КЛЮЧЕВЫЕ ВЫВОДЫ
================================================================================
XGBoost:
Recall класса 0: 0.230 -> 0.330 (↑)
Recall класса 1: 0.835 -> 0.671 (↓)
F1 Macro: 0.500 -> 0.475 (↓)
ROC-AUC: 0.539 -> 0.549 (↑)
LightGBM:
Recall класса 0: 0.228 -> 0.422 (↑)
Recall класса 1: 0.798 -> 0.610 (↓)
F1 Macro: 0.477 -> 0.495 (↑)
ROC-AUC: 0.539 -> 0.541 (↑)
CatBoost:
Recall класса 0: 0.220 -> 0.278 (↑)
Recall класса 1: 0.850 -> 0.809 (↓)
F1 Macro: 0.488 -> 0.510 (↑)
ROC-AUC: 0.614 -> 0.596 (↓)