lale.lib.sklearn.gaussian_nb module

class lale.lib.sklearn.gaussian_nb.GaussianNB(*, priors=None, var_smoothing=1e-09)

Bases: PlannedIndividualOp

Gaussian Naive Bayes classifier from scikit-learn.

This documentation is auto-generated from JSON schemas.

Parameters

• priors (union type, not for optimizer, default None) –

  Prior probabilities of the classes. If specified the priors are not

  • array of items : float

  • or None

• var_smoothing (float, >=0.0 for optimizer, <=1.0 for optimizer, optional, not for optimizer, default 1e-09) – Portion of the largest variance of all features that is added to variances for calculation stability.

Notes

constraint-1 : negated type of ‘X/isSparse’

A sparse matrix was passed, but dense data is required. Use X.toarray() to convert to a dense numpy array.

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

• X (array of items : array of items : float) –

• y (union type) –

  • array of items : string

  • or array of items : float

  • or array of items : boolean

• sample_weight (union type, optional, default None) –

  Weights applied to individual samples.

  • array of items : float

  • or None

    Uniform weights.

partial_fit(X, y=None, **fit_params)

Incremental fit to train train the operator on a batch of samples.

Note: The partial_fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

• X (array of items : array of items : float) –

• y (union type) –

  • array of items : string

  • or array of items : float

  • or array of items : boolean

• classes (union type, optional) –

  • array of items : string

  • or array of items : float

  • or array of items : boolean

• sample_weight (union type, optional, default None) –

  Weights applied to individual samples.

  • array of items : float

  • or None

    Uniform weights.

predict(X, **predict_params)

Make predictions.

Note: The predict method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (array of items : array of items : float) –

Returns

result –

• array of items : string

• or array of items : float

• or array of items : boolean

Return type

union type

predict_proba(X)

Probability estimates for all classes.

Note: The predict_proba method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (array of items : array of items : float) –

Returns

result – Returns the probability of the samples for each class in

Return type

array of items : array of items : float