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| CatBoost: | |
| ensemble: | |
| learning_rate: 0.03 | |
| depth: 6 | |
| loss_function: Logloss | |
| random_seed: 0 | |
| l2_leaf_reg: 3 | |
| subsample: 0.9 | |
| grow_policy: Lossguide # SymmetricTree or Depthwise or Lossguide | |
| bagging_temperature: 1 | |
| random_strength: 3 | |
| min_data_in_leaf: 25 | |
| iterations: 10000 | |
| early_stopping_rounds: 50 | |
| custom_loss: ['AUC', "F1", "Accuracy", "Precision", "Recall", "BrierScore", "Logloss"] | |
| logging_level: 'Silent' # or 'Verbose', 'Info', 'Debug' | |
| train_dir: '/tmp' # avoid write permission issues | |
| auto_class_weights: Balanced # or None, or SqrtBalanced | |
| single_model: | |
| # in this mode, the model is trained on the entire dataset using the best_iter obtained from cross-validation | |
| learning_rate: 0.03 | |
| depth: 6 | |
| loss_function: Logloss | |
| random_seed: 0 | |
| l2_leaf_reg: 3 | |
| subsample: 0.9 | |
| grow_policy: Lossguide # SymmetricTree or Depthwise or Lossguide | |
| bagging_temperature: 1 | |
| random_strength: 3 | |
| min_data_in_leaf: 25 | |
| custom_loss: ['AUC', "F1", "Accuracy", "Precision", "Recall", "BrierScore", "Logloss"] | |
| logging_level: 'Silent' # or 'Verbose', 'Info', 'Debug' | |
| train_dir: '/tmp' # avoid write permission issues | |
| auto_class_weights: Balanced # or None, or SqrtBalanced |