What transformers are available?

Missing Data Imputation

Replace missing (NaN) values in numerical or categorical features

MeanMedianImputer, ArbitraryNumberImputer, RandomSampleImputer, EndTailImputer, CategoricalImputer, AddMissingIndicator, DropMissingData

Categorical Encoding

Convert categorical (string / object) variables to numeric / encoding schemes

OneHotEncoder, OrdinalEncoder, CountFrequencyEncoder, MeanEncoder, WoEEncoder, RareLabelEncoder, DecisionTreeEncoder, StringSimilarityEncoder

Discretisation / Binning

Convert continuous numerical variables into discrete bins or intervals

EqualFrequencyDiscretiser, EqualWidthDiscretiser, DecisionTreeDiscretiser, GeometricWidthDiscretiser, ArbitraryDiscretiser

Outlier Handling / Capping / Trimming

Identify and control extreme values (outliers)

Winsorizer, ArbitraryOutlierCapper, OutlierTrimmer

Variable Transformation

Apply mathematical transformations to numerical features to stabilise variance, reduce skewness, etc.

LogTransformer, LogCpTransformer, ReciprocalTransformer, PowerTransformer, BoxCoxTransformer, YeoJohnsonTransformer, ArcsinTransformer

Feature Creation / Generation

Combine or derive new features from existing ones

MathFeatures, RelativeFeatures, CyclicalFeatures, DecisionTreeFeatures

Datetime / Time-series Features

Extract or generate useful attributes from datetime or temporal data

DatetimeFeatures, DatetimeSubtraction (for differences)

Time Series / Windowing / Lags

For use in forecasting / time-series ML: create lag features, rolling windows, expansions

LagFeatures, WindowFeatures, ExpandingWindowFeatures

Feature Selection / Dropping / Filtering

Methods to drop or select variables based on statistical properties, model performance, correlation, etc.

DropFeatures, DropConstantFeatures, DropDuplicateFeatures, DropCorrelatedFeatures, SmartCorrelatedSelection, SelectByShuffling, SelectBySingleFeaturePerformance, SelectByTargetMeanPerformance, RecursiveFeatureElimination, RecursiveFeatureAddition, SelectByInformationValue, ProbeFeatureSelection, MRMR, etc.

Preprocessing / Matching / Wrapping

Utilities to ensure consistency in variable names, categories, or wrap scikit learn transformers

MatchCategories, MatchVariables, SklearnTransformerWrapper