Scalers#

Different base obj choices for the Scaler are shown below The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”) Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown.

“standard”#

Base Class Documentation: sklearn.preprocessing.StandardScaler

Param Distributions

  1. “base standard”

    with_mean: True
with_std: True

“minmax”#

Base Class Documentation: sklearn.preprocessing.MinMaxScaler

Param Distributions

  1. “base minmax”

    feature_range: (0, 1)

“maxabs”#

Base Class Documentation: sklearn.preprocessing.MaxAbsScaler

Param Distributions

  1. “default”

    defaults only

“robust”#

Base Class Documentation: sklearn.preprocessing.RobustScaler

Param Distributions

  1. “base robust”

    quantile_range: (5, 95)

  2. “robust gs”

    quantile_range: TransitionChoice([(1, 99), (2, 98), (3, 97), (4, 96), (5, 95), (6, 94), (7, 93), (8, 92), (9, 91), (10, 90), (11, 89), (12, 88), (13, 87), (14, 86), (15, 85), (16, 84), (17, 83), (18, 82), (19, 81), (20, 80), (21, 79), (22, 78), (23, 77), (24, 76), (25, 75), (26, 74), (27, 73), (28, 72), (29, 71), (30, 70), (31, 69), (32, 68), (33, 67), (34, 66), (35, 65), (36, 64), (37, 63), (38, 62), (39, 61)])

“yeo”#

Base Class Documentation: sklearn.preprocessing.PowerTransformer

Param Distributions

  1. “base yeo”

    method: 'yeo-johnson'
standardize: True

“boxcox”#

Base Class Documentation: sklearn.preprocessing.PowerTransformer

Param Distributions

  1. “base boxcox”

    method: 'box-cox'
standardize: True

“winsorize”#

Base Class Documentation: BPt.extensions.scalers.Winsorizer

Param Distributions

  1. “base winsorize”

    quantile_range: (1, 99)

  2. “winsorize gs”

    quantile_range: TransitionChoice([(1, 99), (2, 98), (3, 97), (4, 96), (5, 95), (6, 94), (7, 93), (8, 92), (9, 91), (10, 90), (11, 89), (12, 88), (13, 87), (14, 86), (15, 85), (16, 84), (17, 83), (18, 82), (19, 81), (20, 80), (21, 79), (22, 78), (23, 77), (24, 76), (25, 75), (26, 74), (27, 73), (28, 72), (29, 71), (30, 70), (31, 69), (32, 68), (33, 67), (34, 66), (35, 65), (36, 64), (37, 63), (38, 62), (39, 61)])

“quantile norm”#

Base Class Documentation: sklearn.preprocessing.QuantileTransformer

Param Distributions

  1. “base quant norm”

    output_distribution: 'normal'

“quantile uniform”#

Base Class Documentation: sklearn.preprocessing.QuantileTransformer

Param Distributions

  1. “base quant uniform”

    output_distribution: 'uniform'

“normalize”#

Base Class Documentation: sklearn.preprocessing.Normalizer

Param Distributions

  1. “default”

    defaults only