ImportanceCutoffSelector
- class lightautoml.pipelines.selection.importance_based.ImportanceCutoffSelector(feature_pipeline, ml_algo, imp_estimator, fit_on_holdout=True, cutoff=0.0)[source]
Bases:
SelectionPipeline
Selector based on importance threshold.
It is important that data which passed to
.fit
should be ok to fit ml_algo or preprocessing pipeline should be defined.- Parameters:
feature_pipeline (
Optional
[FeaturesPipeline
]) – Composition of feature transforms.ml_algo (
MLAlgo
) – Tuple (MlAlgo, ParamsTuner).imp_estimator (
ImportanceEstimator
) – Feature importance estimator.fit_on_holdout (
bool
) – If use the holdout iterator.cutoff (
float
) – Threshold to cut-off features.
- perform_selection(train_valid=None)[source]
Select features based on cutoff value.
- Parameters:
train_valid (
Optional
[TrainValidIterator
]) – Not used.