An ensemble classifier that trains Decision Trees (Classification Trees or Extra Trees) on random subsets (bootstrap set) of the training data. Predictions are based on the probability scores returned from each tree in the forest, averaged and weighted equally.
Note: The default base tree learner is a fully grown Classification Tree.
Data Type Compatibility: Categorical, Continuous
|1||base||ClassificationTree||Learner||The base tree learner.|
|2||estimators||100||int||The number of learners to train in the ensemble.|
|3||ratio||0.2||float||The ratio of random samples (between 0 and 1.5) to train each base learner on.|
|4||balanced||false||bool||Should we sample the bootstrap set to compensate for imbalanced class labels?|
Return the normalized feature importances i.e. the proportion that each feature contributes to the overall model, indexed by feature column:
public featureImportances() : array
use Rubix\ML\Classifiers\RandomForest; use Rubix\ML\Classifiers\ClassificationTree; $estimator = new RandomForest(new ClassificationTree(10), 300, 0.1, true);
- L. Breiman. (2001). Random Forests.
- L. Breiman et al. (2005). Extremely Randomized Trees.