| models: | |
| logistic_regression: | |
| C: 0.4 | |
| max_iter: 1000 | |
| class_weight: balanced | |
| solver: lbfgs | |
| # High regularization path for stable hybrid ensemble (see stable_training.yaml) | |
| logistic_regression_stable: | |
| C: 0.01 | |
| max_iter: 2000 | |
| class_weight: balanced | |
| solver: lbfgs | |
| random_forest: | |
| n_estimators: 100 | |
| max_depth: 10 | |
| min_samples_split: 10 | |
| min_samples_leaf: 5 | |
| max_features: sqrt | |
| class_weight: balanced | |
| n_jobs: -1 | |
| xgboost: | |
| n_estimators: 100 | |
| max_depth: 3 | |
| learning_rate: 0.1 | |
| subsample: 0.8 | |
| colsample_bytree: 0.8 | |
| min_child_weight: 5 | |
| reg_lambda: 1 | |
| scale_pos_weight: 1 | |
| evaluation: | |
| primary_metric: f1_weighted | |
| metrics: | |
| - accuracy | |
| - f1_weighted | |
| - precision_weighted | |
| - recall_weighted | |
| - roc_auc |