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.
Data Type Compatibility: Depends on base learners
|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)));
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.