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Committee Machine#

A voting ensemble that aggregates the predictions of a committee of heterogeneous learners (referred to as experts). The committee employs a user-specified influence scheme to weight the final predictions.


Influence values can be on any arbitrary scale as they are automatically normalized upon instantiation.

Interfaces: Estimator, Learner, Parallel, Persistable

Data Type Compatibility: Depends on the base learners


# Name Default Type Description
1 experts array An array of learner instances that will comprise the committee.
2 influences null array The influence values for each expert in the committee. If null, each expert will be weighted equally.


use Rubix\ML\CommitteeMachine;
use Rubix\ML\Classifiers\GaussianNB;
use Rubix\ML\Classifiers\RandomForest;
use Rubix\ML\Classifiers\ClassificationTree;
use Rubix\ML\Classifiers\KDNeighbors;
use Rubix\ML\Classifiers\SoftmaxClassifier;

$estimator = new CommitteeMachine([
    new GaussianNB(),
    new RandomForest(new ClassificationTree(4), 100, 0.3),
    new KDNeighbors(3),
    new SoftmaxClassifier(100),
], [
    0.2, 0.4, 0.3, 0.1,

Additional Methods#

Return the learner instances of the committee:

public experts() : array

Return the normalized influence scores of each expert in the committee:

public influences() : array


  1. H. Drucker. (1997). Fast Committee Machines for Regression and Classification.