Multi-output regressor

Fits one regressor for each target of outputs. Useful for extending regressors that do not natively support multiple outputs

Configuration:

• n_jobs

  The number of jobs to run in parallel for fit. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See n_jobs for more details.

  When individual estimators are fast to train or predict using n_jobs>1 can result in slower performance due to the overhead of spawning processes.

Attributes: Inputs:

model : model

model

Outputs:

out-model : model

Output model

Input ports:

model:

model

model

Output ports:

out-model:

model

Output model

Configuration:

n_jobs

The number of jobs to run in parallel for fit. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See n_jobs for more details.

When individual estimators are fast to train or predict using n_jobs>1 can result in slower performance due to the overhead of spawning processes.

Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.

class node_ensemble.MultiOutputRegressor