NpIterativeFeatureSelector
- class lightautoml.pipelines.selection.permutation_importance_based.NpIterativeFeatureSelector(feature_pipeline, ml_algo=None, imp_estimator=None, fit_on_holdout=True, feature_group_size=5, max_features_cnt_in_result=None)[source]
Bases:
lightautoml.pipelines.selection.base.SelectionPipeline
Select features sequentially using chunks to find the best combination of chunks.
The general idea of this algorithm is to sequentially check groups of features ordered by feature importances and if the quality of the model becomes better, we select such group, if not - ignore group.
- __init__(feature_pipeline, ml_algo=None, imp_estimator=None, fit_on_holdout=True, feature_group_size=5, max_features_cnt_in_result=None)[source]
- Parameters
feature_pipeline (
FeaturesPipeline
) – Composition of feature transforms.imp_estimator (
Optional
[ImportanceEstimator
]) – Feature importance estimator.fit_on_holdout (
bool
) – If use the holdout iterator.max_features_cnt_in_result (
Optional
[int
]) – Lower bound of features after selection, if it is reached, it will stop.
- perform_selection(train_valid=None)[source]
Select features iteratively by checking model quality for current selected feats and new group.
- Parameters
train_valid (
Optional
[TrainValidIterator
]) – Iterator for dataset.