edaflow.ml.optimize_hyperparameters
- edaflow.ml.optimize_hyperparameters(model: BaseEstimator, param_distributions: Dict[str, Any], X_train: DataFrame, y_train: Series, cv: int = 5, scoring: str = 'auto', n_iter: int = 50, method: str = 'random', verbose: bool = True, random_state: int = 42) Dict[str, Any][source]
Optimize hyperparameters using various search strategies.
Parameters:
- modelBaseEstimator
The base model to optimize
- param_distributionsDict[str, Any]
Parameter distributions to search over
- X_trainpd.DataFrame
Training features
- y_trainpd.Series
Training target
- cvint, default=5
Number of cross-validation folds
- scoringstr, default=’auto’
Scoring metric (‘auto’ detects based on problem type)
- n_iterint, default=50
Number of iterations for random/bayesian search
- methodstr, default=’random’
Search method (‘grid’, ‘random’, ‘bayesian’)
- verbosebool, default=True
Whether to print optimization progress
- random_stateint, default=42
Random seed for reproducibility
Returns:
- Dict[str, Any]
Dictionary containing best model, parameters, and optimization results