edaflow.ml.compare_models

edaflow.ml.compare_models(models: Dict[str, BaseEstimator], X_train: DataFrame | None = None, X_val: DataFrame | None = None, X_test: DataFrame | None = None, y_train: Series | None = None, y_val: Series | None = None, y_test: Series | None = None, experiment_config: Dict[str, Any] | None = None, problem_type: str = 'auto', metrics: List[str] | None = None, cv_folds: int = 5, scoring: str | List[str] | None = None, verbose: bool = True) → DataFrame[source]

Compare multiple models across various performance metrics.

Parameters:

modelsDict[str, BaseEstimator]: Dictionary of model name -> fitted model pairs
X_trainpd.DataFrame, optional: Training features (can be provided via experiment_config)
X_valpd.DataFrame, optional: Validation features (can be provided via experiment_config)
X_testpd.DataFrame, optional: Test features for final evaluation
y_trainpd.Series, optional: Training target (can be provided via experiment_config)
y_valpd.Series, optional: Validation target (can be provided via experiment_config)
y_testpd.Series, optional: Test target for final evaluation
experiment_configDict[str, Any], optional: Complete experiment configuration from setup_ml_experiment() If provided, will extract X_train, X_val, y_train, y_val from it
problem_typestr, default=’auto’: ‘classification’, ‘regression’, or ‘auto’ to detect
metricsList[str], optional: Specific metrics to calculate. If None, uses default metrics
cv_foldsint, default=5: Number of cross-validation folds (if applicable)
scoringstr or List[str], optional: Scoring metric(s) to use for evaluation
verbosebool, default=True: Whether to print comparison progress