Voting Classifier

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 joblib.parallel_backend context. -1 means using all processors. See n_jobs for more details.

Added in version 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

class node_ensemble.VotingClassifier