Gradient Boost#

Gradient Boost is a stage-wise additive ensemble that uses a Gradient Descent boosting scheme for training boosters (Decision Trees) to correct the error residuals of a series of weak base learners. Stochastic gradient boosting is achieved by varying the ratio of samples to subsample uniformly at random from the training set.

Note: The default base regressor is a Dummy Regressor using the Mean strategy and the default booster is a Regression Tree with a max depth of 3.

Interfaces: Estimator, Learner, Verbose, Ranks Features, Persistable

Data Type Compatibility: Depends on base learners


# Param Default Type Description
1 booster RegressionTree Learner The regressor that will fix up the error residuals of the weak base learner.
2 rate 0.1 float The learning rate of the ensemble i.e. the shrinkage applied to each step.
3 ratio 0.5 float The ratio of samples to subsample from the training set to train each booster.
4 estimators 1000 int The maximum number of boosters to train in the ensemble.
5 min change 1e-4 float The minimum change in the training loss necessary to continue training.
6 window 10 int The number of epochs without improvement in the validation score to wait before considering an early stop.
7 holdout 0.1 float The proportion of training samples to use for validation and progress monitoring.
8 metric RMSE Metric The metric used to score the generalization performance of the model during training.
9 base DummyRegressor Learner The weak base learner to be boosted.


use Rubix\ML\Regressors\GradientBoost;
use Rubix\ML\Regressors\RegressionTree;
use Rubix\ML\CrossValidation\Metrics\SMAPE;
use Rubix\ML\Regressors\DummyRegressor;
use Rubix\ML\Other\Strategies\Constant;

$estimator = new GradientBoost(new RegressionTree(3), 0.1, 0.8, 1000, 1e-4, 10, 0.1, new SMAPE(), new DummyRegressor(new Constant(0.0)));

Additional Methods#

Return the validation score at each epoch from the last training session:

public scores() : float[]|null

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

public steps() : float[]|null


  • J. H. Friedman. (2001). Greedy Function Approximation: A Gradient Boosting Machine.
  • J. H. Friedman. (1999). Stochastic Gradient Boosting.
  • Y. Wei. et al. (2017). Early stopping for kernel boosting algorithms: A general analysis with localized complexities.