lale.lib.rasl.batched_bagging_classifier module

class lale.lib.rasl.batched_bagging_classifier.BatchedBaggingClassifier(*, base_estimator=None)

Bases: PlannedIndividualOp

Implementation of a homomorphic bagging classifier.

This documentation is auto-generated from JSON schemas.

As proposed in https://izbicki.me/public/papers/icml2013-algebraic-classifiers.pdf

Parameters

base_estimator (union type, not for optimizer, default None) –

Planned Lale individual operator or pipeline.

• operator

• or None

  lale.lib.sklearn.LogisticRegression

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) – The training input samples. Sparse matrices are accepted only if

• y (union type) –

  The target values (class labels).

  • array of items : float

  • or array of items : string

  • or array of items : boolean

• sample_weight (union type, optional) –

  Sample weights. If None, then samples are equally weighted.

  • array of items : float

  • or None

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:

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