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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 trained and given an influence score inversely proportional to the loss it incurs at that epoch.


The default base learner is a Classification Tree with a max height of 1 i.e a Decision Stump.

Interfaces: Estimator, Learner, Probabilistic, Verbose, Persistable

Data Type Compatibility: Depends on base learner


# Name Default Type Description
1 base ClassificationTree Learner 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 epochs 100 int The maximum number of training epochs. i.e. the number of times to iterate before terminating.
5 minChange 1e-4 float The minimum change in the training loss necessary to continue training.
6 window 5 int The number of epochs without improvement in the training loss to wait before considering an early stop.


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

$estimator = new AdaBoost(new ExtraTreeClassifier(3), 0.1, 0.5, 200, 1e-3, 10);

Additional Methods#

Return an iterable progress table with the steps from the last training session:

public steps() : iterable

use Rubix\ML\Extractors\CSV;

$extractor = new CSV('progress.csv', true);


Return the loss for each epoch from the last training session:

public losses() : float[]|null


  1. Y. Freund et al. (1996). A Decision-theoretic Generalization of On-line Learning and an Application to Boosting. 

  2. J. Zhu et al. (2006). Multi-class AdaBoost.