Multi-output Classifier¶
Fits one classifier for each target of outputs. Useful for extending classifiers that do not natively support multiple outputs
Documentation
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 run in parallel.
fit()
,predict()
andpartial_fit()
(if supported by the passed estimator) will be parallelized for each target.When individual estimators are fast to train or predict, using
n_jobs > 1
can result in slower performance due to the parallelism overhead.
None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all available processes / threads. See n_jobs for more details.Changed in version 0.20: n_jobs default changed from 1 to None.
Attributes:
- Input ports:
- modelmodel
model
- Output ports:
- out-modelmodel
Output model
Definition
Input ports
- model
1 - 1, model
model
Output ports
- out-model
model
Output model
- class node_ensemble.MultiOutputClassifier[source]