lale.lib.sklearn.multi_output_regressor module¶

class lale.lib.sklearn.multi_output_regressor.MultiOutputRegressor(*, estimator=None, n_jobs=None)¶

Multi-output regressor from scikit-learn for multi target regression.

This documentation is auto-generated from JSON schemas.

Parameters

estimator (union type, not for optimizer, default None) –
An estimator object implementing fit and predict.
- operator
- or None
n_jobs (union type, not for optimizer, default None) –
The number of jobs to run in parallel for fit, predict, and partial_fit (if supported by the passed estimator).
- None
  
  1 unless in joblib.parallel_backend context.
- or -1
  
  Use all processors.
- or integer, >=1
  
  Number of CPU cores.

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 (array of items : array of items : float) – The target values (real numbers).
sample_weight (union type, optional, default None) –
Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.
- 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: