edaflow.ml.configure_model_pipeline

edaflow.ml.configure_model_pipeline(data_config: Dict[str, Any], numerical_strategy: str = 'standard', categorical_strategy: str = 'onehot', handle_missing: str = 'drop', verbose: bool = True) → Pipeline

Configure a preprocessing pipeline for the ML experiment.

Parameters:

data_configDict[str, Any]

Configuration dictionary from setup_ml_experiment

numerical_strategystr, default=’standard’

Scaling strategy for numerical features (‘standard’, ‘minmax’, ‘robust’, ‘none’)

categorical_strategystr, default=’onehot’

Encoding strategy for categorical features (‘onehot’, ‘target’, ‘none’)

handle_missingstr, default=’drop’

Missing value strategy (‘drop’, ‘impute’, ‘flag’)

verbosebool, default=True

Whether to print pipeline configuration details

Returns:

Pipeline

Configured sklearn Pipeline for preprocessing