| 102 | return self.best_model |
| 103 | |
| 104 | class HyperparameterTuner: |
| 105 | |
| 106 | |
| 107 | def tune_hyperparameters(self, model: BaseEstimator, X_train: np.ndarray, |
| 108 | y_train: np.ndarray) -> BaseEstimator: |
| 109 | |
| 110 | # Example hyperparameter grid for RandomForestClassifier |
| 111 | if isinstance(model, RandomForestClassifier): |
| 112 | param_grid = { |
| 113 | 'n_estimators': [100, 200, 300], |
| 114 | 'max_depth': [10, 20, 30], |
| 115 | 'min_samples_split': [2, 5, 10] |
| 116 | } |
| 117 | return model |
| 118 | |
| 119 | class ModelEnsemble: |
| 120 |