scikit-learn preprocessing wrappers, mirroring TuiML's native layout.
preprocessing, so a wrapped transformer sits where its native counterpart does. Registered under sklearn. hub keys, and usable as pipeline steps alongside native components.scaling — StandardScaler,
MinMaxScaler, MaxAbsScaler, RobustScaler, QuantileTransformer, PowerTransformer, Normalizer, KBinsDiscretizer, PolynomialFeatures, SplineTransformer, and the encoders (OneHotEncoder, OrdinalEncoder, TargetEncoder).
imputation — SimpleImputer,
KNNImputer, IterativeImputer, MissingIndicator.
text — CountVectorizer,
TfidfVectorizer, TfidfTransformer, HashingVectorizer.