API Reference / sklearn /

linear.py

scikit-learn linear wrappers.

Generated from the LINEAR table in specs. Registered under sklearn. hub keys, mirroring the native TuiML linear family.

Classes

DummyClassifier

class sklearn.linear.DummyClassifier(_SklearnBackedMixin, Classifier)

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

Wraps DummyClassifier. 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.

LabelPropagation

class sklearn.linear.LabelPropagation(_SklearnBackedMixin, Classifier)

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

Wraps LabelPropagation. 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.

LabelSpreading

class sklearn.linear.LabelSpreading(_SklearnBackedMixin, Classifier)

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

Wraps LabelSpreading. 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.

LinearDiscriminantAnalysis

class sklearn.linear.LinearDiscriminantAnalysis(_SklearnBackedMixin, Classifier)

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

Wraps LinearDiscriminantAnalysis. 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.

LogisticRegression

class sklearn.linear.LogisticRegression(_SklearnBackedMixin, Classifier)

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

Wraps LogisticRegression. 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: C, penalty, solver, max_iter.

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.

LogisticRegressionCV

class sklearn.linear.LogisticRegressionCV(_SklearnBackedMixin, Classifier)

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

Wraps LogisticRegressionCV. 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.

PassiveAggressiveClassifier

class sklearn.linear.PassiveAggressiveClassifier(_SklearnBackedMixin, Classifier)

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

Wraps PassiveAggressiveClassifier. 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.

Perceptron

class sklearn.linear.Perceptron(_SklearnBackedMixin, Classifier)

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

Wraps Perceptron. 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.

QuadraticDiscriminantAnalysis

class sklearn.linear.QuadraticDiscriminantAnalysis(_SklearnBackedMixin, Classifier)

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

Wraps QuadraticDiscriminantAnalysis. 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.

RidgeClassifier

class sklearn.linear.RidgeClassifier(_SklearnBackedMixin, Classifier)

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

Wraps RidgeClassifier. 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.

RidgeClassifierCV

class sklearn.linear.RidgeClassifierCV(_SklearnBackedMixin, Classifier)

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

Wraps RidgeClassifierCV. 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.

SGDClassifier

class sklearn.linear.SGDClassifier(_SklearnBackedMixin, Classifier)

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

Wraps SGDClassifier. 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.

ARDRegression

class sklearn.linear.ARDRegression(_SklearnBackedMixin, Regressor)

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

Wraps ARDRegression. 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.

BayesianRidge

class sklearn.linear.BayesianRidge(_SklearnBackedMixin, Regressor)

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

Wraps BayesianRidge. 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.

DummyRegressor

class sklearn.linear.DummyRegressor(_SklearnBackedMixin, Regressor)

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

Wraps DummyRegressor. 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.

ElasticNet

class sklearn.linear.ElasticNet(_SklearnBackedMixin, Regressor)

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

Wraps ElasticNet. 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: alpha, l1_ratio.

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.

ElasticNetCV

class sklearn.linear.ElasticNetCV(_SklearnBackedMixin, Regressor)

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

Wraps ElasticNetCV. 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.

GammaRegressor

class sklearn.linear.GammaRegressor(_SklearnBackedMixin, Regressor)

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

Wraps GammaRegressor. 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.

HuberRegressor

class sklearn.linear.HuberRegressor(_SklearnBackedMixin, Regressor)

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

Wraps HuberRegressor. 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.

KernelRidge

class sklearn.linear.KernelRidge(_SklearnBackedMixin, Regressor)

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

Wraps KernelRidge. 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.

Lars

class sklearn.linear.Lars(_SklearnBackedMixin, Regressor)

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

Wraps Lars. 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.

LarsCV

class sklearn.linear.LarsCV(_SklearnBackedMixin, Regressor)

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

Wraps LarsCV. 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.

Lasso

class sklearn.linear.Lasso(_SklearnBackedMixin, Regressor)

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

Wraps Lasso. 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: alpha, max_iter.

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.

LassoCV

class sklearn.linear.LassoCV(_SklearnBackedMixin, Regressor)

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

Wraps LassoCV. 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.

LassoLars

class sklearn.linear.LassoLars(_SklearnBackedMixin, Regressor)

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

Wraps LassoLars. 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.

LassoLarsCV

class sklearn.linear.LassoLarsCV(_SklearnBackedMixin, Regressor)

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

Wraps LassoLarsCV. 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.

LassoLarsIC

class sklearn.linear.LassoLarsIC(_SklearnBackedMixin, Regressor)

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

Wraps LassoLarsIC. 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.

LinearRegression

class sklearn.linear.LinearRegression(_SklearnBackedMixin, Regressor)

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

Wraps LinearRegression. 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.

OrthogonalMatchingPursuit

class sklearn.linear.OrthogonalMatchingPursuit(_SklearnBackedMixin, Regressor)

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

Wraps OrthogonalMatchingPursuit. 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.

OrthogonalMatchingPursuitCV

class sklearn.linear.OrthogonalMatchingPursuitCV(_SklearnBackedMixin, Regressor)

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

Wraps OrthogonalMatchingPursuitCV. 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.

PLSRegression

class sklearn.linear.PLSRegression(_SklearnBackedMixin, Regressor)

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

Wraps PLSRegression. 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.

PassiveAggressiveRegressor

class sklearn.linear.PassiveAggressiveRegressor(_SklearnBackedMixin, Regressor)

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

Wraps PassiveAggressiveRegressor. 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.

PoissonRegressor

class sklearn.linear.PoissonRegressor(_SklearnBackedMixin, Regressor)

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

Wraps PoissonRegressor. 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.

QuantileRegressor

class sklearn.linear.QuantileRegressor(_SklearnBackedMixin, Regressor)

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

Wraps QuantileRegressor. 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.

RANSACRegressor

class sklearn.linear.RANSACRegressor(_SklearnBackedMixin, Regressor)

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

Wraps RANSACRegressor. 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.

Ridge

class sklearn.linear.Ridge(_SklearnBackedMixin, Regressor)

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

Wraps Ridge. 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: alpha, solver.

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.

RidgeCV

class sklearn.linear.RidgeCV(_SklearnBackedMixin, Regressor)

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

Wraps RidgeCV. 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.

SGDRegressor

class sklearn.linear.SGDRegressor(_SklearnBackedMixin, Regressor)

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

Wraps SGDRegressor. 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.

TheilSenRegressor

class sklearn.linear.TheilSenRegressor(_SklearnBackedMixin, Regressor)

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

Wraps TheilSenRegressor. 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.

TweedieRegressor

class sklearn.linear.TweedieRegressor(_SklearnBackedMixin, Regressor)

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

Wraps TweedieRegressor. 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.