Multi-output regressor

../../../../_images/multioutput.svg

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:

Input ports:
model : model
model
Output ports:
out-model : model
Output model
n_jobs (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[source]