Voting Classifier

../../../../_images/votingclassifier.svg

Uses voting to select answer from multiple classifiers. Add additional input ports for models by right-clicking on node and selecting “Create Input Port > models”

Configuration:

  • names

    Comma separated list of model names, eg. Rescale, SVC

  • copies

    Number of copies to make of each input model

  • 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.

  • voting

    If ‘hard’, uses predicted class labels for majority rule voting. Else if ‘soft’, predicts the class label based on the argmax of the sums of the predicted probabilities, which is recommended for an ensemble of well-calibrated classifiers.

Attributes:

  • classes_

    The classes labels.

Input ports:
models : model
models
Output ports:
out-model : model
Output model
names (names)
Comma separated list of model names, eg. Rescale, SVC
copies (copies)
Number of copies to make of each input 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.
voting (voting)
If ‘hard’, uses predicted class labels for majority rule voting. Else if ‘soft’, predicts the class label based on the argmax of the sums of the predicted probabilities, which is recommended for an ensemble of well-calibrated classifiers.

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.VotingClassifier[source]