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Bootstrap Aggregator#

Bootstrap Aggregating (or bagging for short) is a model averaging technique designed to improve the stability and performance of a user-specified base estimator by training a number of them on a unique bootstrapped training set sampled at random with replacement. Bagging works well with estimators that tend to have high variance by controlling the variance through averaging.

Note: Not compatible with clusterers.

Interfaces: Estimator, Learner, Parallel, Persistable

Data Type Compatibility: Depends on base learner

Parameters#

# Param Default Type Description
1 base object The base estimator to be used in the ensemble.
2 estimators 10 int The number of base estimators to train in the ensemble.
3 ratio 0.5 float The ratio of samples (between 0 and 1.5) from the training set to train each base estimator with.

Additional Methods#

This meta estimator does not have any additional methods.

Example#

use Rubix\ML\BootstrapAggregator;
use Rubix\ML\Regressors\RegressionTree;

$estimator = new BootstrapAggregator(new RegressionTree(20), 300, 0.2);

References#

  • L. Breiman. (1996). Bagging Predictors.