AdaBoost#
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.
Note
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
Parameters#
# | 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 | estimators | 100 | int | The maximum number of weak learners to train in the ensemble. |
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. |
Example#
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);
$extractor->export($estimator->steps());
Return the loss for each epoch from the last training session:
public losses() : float[]|null
References#
Last update:
2021-05-08