API Reference / sklearn /

ensemble.py

scikit-learn ensemble wrappers.

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

Classes

AdaBoostClassifier

class sklearn.ensemble.AdaBoostClassifier(_SklearnBackedMixin, Classifier)

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

Wraps AdaBoostClassifier. 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.

BaggingClassifier

class sklearn.ensemble.BaggingClassifier(_SklearnBackedMixin, Classifier)

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

Wraps BaggingClassifier. 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.

ExtraTreesClassifier

class sklearn.ensemble.ExtraTreesClassifier(_SklearnBackedMixin, Classifier)

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

Wraps ExtraTreesClassifier. 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_estimators, max_depth, random_state.

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.

GradientBoostingClassifier

class sklearn.ensemble.GradientBoostingClassifier(_SklearnBackedMixin, Classifier)

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

Wraps GradientBoostingClassifier. 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_estimators, learning_rate, max_depth.

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.

HistGradientBoostingClassifier

class sklearn.ensemble.HistGradientBoostingClassifier(_SklearnBackedMixin, Classifier)

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

Wraps HistGradientBoostingClassifier. 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: learning_rate, max_iter, max_depth.

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.

RandomForestClassifier

class sklearn.ensemble.RandomForestClassifier(_SklearnBackedMixin, Classifier)

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

Wraps RandomForestClassifier. 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_estimators, max_depth, random_state.

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.

AdaBoostRegressor

class sklearn.ensemble.AdaBoostRegressor(_SklearnBackedMixin, Regressor)

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

Wraps AdaBoostRegressor. 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.

BaggingRegressor

class sklearn.ensemble.BaggingRegressor(_SklearnBackedMixin, Regressor)

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

Wraps BaggingRegressor. 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.

ExtraTreesRegressor

class sklearn.ensemble.ExtraTreesRegressor(_SklearnBackedMixin, Regressor)

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

Wraps ExtraTreesRegressor. 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_estimators, max_depth, random_state.

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.

GradientBoostingRegressor

class sklearn.ensemble.GradientBoostingRegressor(_SklearnBackedMixin, Regressor)

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

Wraps GradientBoostingRegressor. 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_estimators, learning_rate, max_depth.

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.

HistGradientBoostingRegressor

class sklearn.ensemble.HistGradientBoostingRegressor(_SklearnBackedMixin, Regressor)

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

Wraps HistGradientBoostingRegressor. 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: learning_rate, max_iter, max_depth.

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.

RandomForestRegressor

class sklearn.ensemble.RandomForestRegressor(_SklearnBackedMixin, Regressor)

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

Wraps RandomForestRegressor. 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_estimators, max_depth, random_state.

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.

RandomTreesEmbedding

class sklearn.ensemble.RandomTreesEmbedding(_SklearnTransformerMixin, Transformer)

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

Wraps RandomTreesEmbedding. 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.