API Reference / sklearn /

bayesian.py

scikit-learn bayesian wrappers.

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

Classes

BernoulliNB

class sklearn.bayesian.BernoulliNB(_SklearnBackedMixin, Classifier)

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

Wraps BernoulliNB. 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.

CategoricalNB

class sklearn.bayesian.CategoricalNB(_SklearnBackedMixin, Classifier)

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

Wraps CategoricalNB. 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.

ComplementNB

class sklearn.bayesian.ComplementNB(_SklearnBackedMixin, Classifier)

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

Wraps ComplementNB. 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.

GaussianNB

class sklearn.bayesian.GaussianNB(_SklearnBackedMixin, Classifier)

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

Wraps GaussianNB. 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.

GaussianProcessClassifier

class sklearn.bayesian.GaussianProcessClassifier(_SklearnBackedMixin, Classifier)

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

Wraps GaussianProcessClassifier. 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.

MultinomialNB

class sklearn.bayesian.MultinomialNB(_SklearnBackedMixin, Classifier)

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

Wraps MultinomialNB. 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.

GaussianProcessRegressor

class sklearn.bayesian.GaussianProcessRegressor(_SklearnBackedMixin, Regressor)

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

Wraps GaussianProcessRegressor. 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.