Multi-output classifier

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

Configuration:

• n_jobs

  The number of jobs to use for the computation. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used. The number of jobs to use for the computation. It does each target variable in y in parallel.

Attributes: Inputs:

model : model

model

Outputs:

out-model : model

Output model

Ports:

Inputs:

model:

model

model

Outputs:

out-model:

model

Output model

Configuration:

n_jobs

The number of jobs to use for the computation. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used. The number of jobs to use for the computation. It does each target variable in y in parallel.

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.MultiOutputClassifier