gbm.regr

Description

Wrapped learner for Gradient Boosting Machine from package gbm for regression problems.

Details

Common hyperparameters:

n.trees

Total number of trees to fit. This is equivalent to the number of iterations and the number of basis functions in the additive expansion.

interaction.depth

The maximum depth of variable interactions. 1 implies an additive model, 2 implies a model with up to 2-way interactions, etc.

n.minobsinnode

Minimum number of observations in the trees terminal nodes.

shrinkage

Shrinkage parameter applied to each tree in the expansion. Also known as the learning rate or step-size reduction.

bag.fraction

Fraction of the training set observations randomly selected to propose the next tree in the expansion.

Extends

wrapped.learner.regr

Methods

See Also

gbm