lale.lib.snapml.batched_tree_ensemble_regressor module

class lale.lib.snapml.batched_tree_ensemble_regressor.BatchedTreeEnsembleRegressor

Bases: PlannedIndividualOp

Batched Tree Ensemble Regressor from Snap ML.

This documentation is auto-generated from JSON schemas.

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) –

    The outer array is over samples aka rows.

    • items : array of items : float

      The inner array is over features aka columns.

  • y (union type) –

    The classes.

    • array of items : float

    • or array of items : string

    • or array of items : boolean

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

    Sample weights.

    • array of items : float

    • or None

      Samples are equally weighted.

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) –

The outer array is over samples aka rows.

  • items : array of items : float

    The inner array is over features aka columns.

Returns

result – The predicted classes.

  • array of items : float

  • or array of items : string

  • 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, optional) –

The outer array is over samples aka rows.

  • items : array of items : float

    The inner array is over features aka columns.

Returns

result – The outer array is over samples aka rows.

  • items : array of items : float

    The inner array contains probabilities corresponding to each class.

Return type

array