edaflow.ml.bayesian_optimizationο
- edaflow.ml.bayesian_optimization(model: BaseEstimator, param_space: Dict[str, Any], X_train: DataFrame, y_train: Series, n_calls: int = 50, cv: int = 5, scoring: str = 'auto', verbose: bool = True, random_state: int = 42) Dict[str, Any][source]ο
Perform Bayesian optimization using scikit-optimize.
Parameters:ο
- modelBaseEstimator
The base model to optimize
- param_spaceDict[str, Any]
Parameter space definition (requires skopt)
- X_trainpd.DataFrame
Training features
- y_trainpd.Series
Training target
- n_callsint, default=50
Number of optimization calls
- cvint, default=5
Number of cross-validation folds
- scoringstr, default=βautoβ
Scoring metric
- verbosebool, default=True
Whether to print progress
- random_stateint, default=42
Random seed for reproducibility
Returns:ο
- Dict[str, Any]
Optimization results including best parameters and convergence plot