LGBAdvancedPipeline
- class lightautoml.pipelines.features.lgb_pipeline.LGBAdvancedPipeline(feats_imp=None, top_intersections=5, max_intersection_depth=3, subsample=None, multiclass_te_co=3, auto_unique_co=10, output_categories=False, fill_na=False, ascending_by_cardinality=False, use_groupby=False, groupby_types=['delta_median', 'delta_mean', 'min', 'max', 'std', 'mode', 'is_mode'], groupby_triplets=[], groupby_top_based_on='cardinality', groupby_top_categorical=3, groupby_top_numerical=3, **kwargs)[source]
Bases:
FeaturesPipeline,TabularDataFeaturesCreate advanced pipeline for trees based models.
Includes:
Different cats and numbers handling according to role params.
Dates handling - extracting seasons and create datediffs.
Create categorical intersections.
- Parameters:
feats_imp (
Union[ImportanceEstimator,SelectionPipeline,None]) – Features importances mapping.top_intersections (
int) – Max number of categories to generate intersections.max_intersection_depth (
int) – Max depth of cat intersection.subsample (
Union[float,int,None]) – Subsample to calc data statistics.multiclass_te_co (
int) – Cutoff if use target encoding in cat handling on multiclass task if number of classes is high.auto_unique_co (
int) – Switch to target encoding if high cardinality.
- create_pipeline(train)[source]
Create tree pipeline.
- Parameters:
train (
Union[PandasDataset,NumpyDataset]) – Dataset with train features.- Return type:
- Returns:
Transformer.