1<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/CommitteeMachine.php">[source]</a></span> 2 3# Committee Machine 4A 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. 5 6!!! note 7 Influence values can be on any arbitrary scale as they are automatically normalized upon instantiation. 8 9**Interfaces:** [Estimator](estimator.md), [Learner](learner.md), [Parallel](parallel.md), [Verbose](verbose.md), [Persistable](persistable.md) 10 11**Data Type Compatibility:** Depends on the base learners 12 13## Parameters 14| # | Name | Default | Type | Description | 15|---|---|---|---|---| 16| 1 | experts | | array | An array of learner instances that will comprise the committee. | 17| 2 | influences | null | array | The influence values for each expert in the committee. If null, each expert will be weighted equally. | 18 19## Example 20```php 21use Rubix\ML\CommitteeMachine; 22use Rubix\ML\Classifiers\GaussianNB; 23use Rubix\ML\Classifiers\RandomForest; 24use Rubix\ML\Classifiers\ClassificationTree; 25use Rubix\ML\Classifiers\KDNeighbors; 26use Rubix\ML\Classifiers\SoftmaxClassifier; 27 28$estimator = new CommitteeMachine([ 29 new GaussianNB(), 30 new RandomForest(new ClassificationTree(4), 100, 0.3), 31 new KDNeighbors(3), 32 new SoftmaxClassifier(100), 33], [ 34 0.2, 0.4, 0.3, 0.1, 35]); 36``` 37 38## Additional Methods 39Return the learner instances of the committee: 40```php 41public experts() : array 42``` 43 44Return the normalized influence scores of each expert in the committee: 45```php 46public influences() : array 47``` 48 49## References 50[^1]: H. Drucker. (1997). Fast Committee Machines for Regression and Classification.