API Reference / sklearn / preprocessing /

scaling.py

scikit-learn scaling wrappers.

Generated from the PREPROCESSING_SCALING table in specs. Registered under sklearn. hub keys, mirroring the native TuiML preprocessing/scaling family.

Classes

Binarizer

class sklearn.preprocessing.scaling.Binarizer(_SklearnTransformerMixin, Transformer)

scikit-learn Binarizer (hub key sklearn.Binarizer).

Wraps Binarizer. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

FunctionTransformer

class sklearn.preprocessing.scaling.FunctionTransformer(_SklearnTransformerMixin, Transformer)

scikit-learn FunctionTransformer (hub key sklearn.FunctionTransformer).

Wraps FunctionTransformer. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

KBinsDiscretizer

class sklearn.preprocessing.scaling.KBinsDiscretizer(_SklearnTransformerMixin, Transformer)

scikit-learn KBinsDiscretizer (hub key sklearn.KBinsDiscretizer).

Wraps KBinsDiscretizer. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.

Commonly set: n_bins, encode, strategy.

Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

MaxAbsScaler

class sklearn.preprocessing.scaling.MaxAbsScaler(_SklearnTransformerMixin, Transformer)

scikit-learn MaxAbsScaler (hub key sklearn.MaxAbsScaler).

Wraps MaxAbsScaler. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

MinMaxScaler

class sklearn.preprocessing.scaling.MinMaxScaler(_SklearnTransformerMixin, Transformer)

scikit-learn MinMaxScaler (hub key sklearn.MinMaxScaler).

Wraps MinMaxScaler. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.

Commonly set: feature_range.

Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

Normalizer

class sklearn.preprocessing.scaling.Normalizer(_SklearnTransformerMixin, Transformer)

scikit-learn Normalizer (hub key sklearn.Normalizer).

Wraps Normalizer. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

OneHotEncoder

class sklearn.preprocessing.scaling.OneHotEncoder(_SklearnTransformerMixin, Transformer)

scikit-learn OneHotEncoder (hub key sklearn.OneHotEncoder).

Wraps OneHotEncoder. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.

Commonly set: handle_unknown, drop, sparse_output.

Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

OrdinalEncoder

class sklearn.preprocessing.scaling.OrdinalEncoder(_SklearnTransformerMixin, Transformer)

scikit-learn OrdinalEncoder (hub key sklearn.OrdinalEncoder).

Wraps OrdinalEncoder. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

PolynomialFeatures

class sklearn.preprocessing.scaling.PolynomialFeatures(_SklearnTransformerMixin, Transformer)

scikit-learn PolynomialFeatures (hub key sklearn.PolynomialFeatures).

Wraps PolynomialFeatures. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.

Commonly set: degree, interaction_only, include_bias.

Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

PowerTransformer

class sklearn.preprocessing.scaling.PowerTransformer(_SklearnTransformerMixin, Transformer)

scikit-learn PowerTransformer (hub key sklearn.PowerTransformer).

Wraps PowerTransformer. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

QuantileTransformer

class sklearn.preprocessing.scaling.QuantileTransformer(_SklearnTransformerMixin, Transformer)

scikit-learn QuantileTransformer (hub key sklearn.QuantileTransformer).

Wraps QuantileTransformer. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

RobustScaler

class sklearn.preprocessing.scaling.RobustScaler(_SklearnTransformerMixin, Transformer)

scikit-learn RobustScaler (hub key sklearn.RobustScaler).

Wraps RobustScaler. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.

Commonly set: with_centering, with_scaling, quantile_range.

Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

SplineTransformer

class sklearn.preprocessing.scaling.SplineTransformer(_SklearnTransformerMixin, Transformer)

scikit-learn SplineTransformer (hub key sklearn.SplineTransformer).

Wraps SplineTransformer. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

StandardScaler

class sklearn.preprocessing.scaling.StandardScaler(_SklearnTransformerMixin, Transformer)

scikit-learn StandardScaler (hub key sklearn.StandardScaler).

Wraps StandardScaler. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.

Commonly set: with_mean, with_std.

Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.

TargetEncoder

class sklearn.preprocessing.scaling.TargetEncoder(_SklearnTransformerMixin, Transformer)

scikit-learn TargetEncoder (hub key sklearn.TargetEncoder).

Wraps TargetEncoder. Accepts that estimator's constructor parameters as keyword arguments; call get_parameter_schema for the full list with types and defaults derived from the installed scikit-learn.
Constructor 
__init__(
    self,
    **params,
)

Methods

__init__ (self, **params)
get_parameter_schema (cls) -> Dict[str, Any]

Return JSON Schema for constructor parameters.

get_capabilities (cls) -> List[str]

Return the capability names this wrapper supports.