Short for Adaptive Boosting, this ensemble classifier can improve the performance of an otherwise weak classifier by focusing more attention on samples that are harder to classify. It builds an additive model where, at each stage, a new learner is instantiated and trained.

Note: The default base classifier is a Classification Tree with a max depth of 1 i.e a Decision Stump.

Interfaces: Estimator, Learner, Probabilistic, Verbose, Persistable

Data Type Compatibility: Depends on base learner


# Param Default Type Description
1 base ClassificationTree object The base weak classifier to be boosted.
2 rate 1.0 float The learning rate of the ensemble i.e. the shrinkage applied to each step.
3 ratio 0.8 float The ratio of samples to subsample from the training set to train each weak learner.
4 estimators 100 int The maximum number of weak learners to train in the ensemble.
5 min change 1e-4 float The minimum change in the training loss necessary to continue training.

Additional Methods#

Return the calculated weight values of the samples in the last training set:

public weights() : array

Return the influence scores for each boosted classifier:

public influences() : array

Return the training loss at each epoch:

public steps() : array


use Rubix\ML\Classifiers\AdaBoost;
use Rubix\ML\Classifiers\ExtraTreeClassifier;

$estimator = new AdaBoost(new ExtraTreeClassifier(3), 0.1, 0.5, 200, 1e-3);


  • Y. Freund et al. (1996). A Decision-theoretic Generalization of On-line Learning and an Application to Boosting.
  • J. Zhu et al. (2006). Multi-class AdaBoost.