CountVectorizer performs bag of words encoding of text.

Identity is a transformer that returns unmodified input value.

KBinsDiscretizer based on quantile strategy.

MaxAbsScaler transforms value into -1 to +1 range linearly.

MinMaxScaler is a transformer that rescales value into range between min and max.

OneHotEncoder encodes string value to corresponding index Mapping should contain all values from 0 to N where N is len(Mapping).

OrdinalEncoder returns 0 for string that is not found, or else a number for that string Mapping should contain all values from 0 to N where N is len(Mapping).

QuantileScaler transforms any distribution to uniform distribution This is done by mapping values to quantiles they belong to.

SampleNormalizerL1 transforms features for single sample to have norm L1=1.

SampleNormalizerL2 transforms features for single sample to have norm L2=1.

StandardScaler transforms feature into normal standard distribution.

TFIDFVectorizer performs tf-idf vectorization on top of count vectorization.