.. _`Voting Classifier`:

.. _`org.sysess.sympathy.machinelearning.votingclassifier`:

Voting Classifier
`````````````````

.. image:: votingclassifier.svg
   :width: 48


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"




Documentation
:::::::::::::

The idea behind the VotingClassifier is to combine conceptually different machine learning
    classifiers and use a majority vote or the average predicted probabilities (soft vote) to
    predict the class labels. Such a classifier can be useful for a set of equally well performing
    models in order to balance out their individual weaknesses.


Attributes
==========

    **classes_**
        The classes labels.


Definition
::::::::::


Input ports
===========

    **models**
        | Type: model
        | Description: models
        | Optional number of ports: 1--inf (default: 1)
        

Output ports
============

    **out-model**
        | Type: model
        | Description: Output model



Configuration
=============

    **Copies** (copies)
        Number of copies to make of each input model
    **Number of jobs** (n_jobs)
        The number of jobs to run in parallel for ``fit``.
        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
        ``-1`` means using all processors. See n_jobs
        for more details.

        .. versionadded:: 0.18
    **Estimators** (names)
        Comma separated list of model names, eg. Rescale, SVC
    **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.













Implementation
==============

.. automodule:: node_ensemble
    :noindex:

.. class:: VotingClassifier
    :noindex: