gb_classifier: GB classifier

In autoMrP: Improving MrP with Ensemble Learning

GB classifier

Description

gb_classifier applies gradient boosting classification to a data set.

Usage

gb_classifier(
  form,
  distribution,
  data.train,
  n.trees,
  interaction.depth,
  n.minobsinnode,
  shrinkage,
  verbose = c(TRUE, FALSE)
)

Arguments

form	Model formula. A two-sided linear formula describing the model to be fit, with the outcome on the LHS and the covariates separated by + operators on the RHS.
distribution	Model distribution. A character string specifying the name of the distribution to be used.
data.train	Training data. A data.frame containing the training data used to train the model.
n.trees	Total number of trees. An integer-valued scalar specifying the total number of trees to be fit.
interaction.depth	Interaction depth. An integer-valued scalar specifying the maximum depth of each tree.
n.minobsinnode	Minimum number of observations in terminal nodes. An integer-valued scalar specifying the minimum number of observations in the terminal nodes of the trees.
shrinkage	Learning rate. A numeric scalar specifying the shrinkage or learning rate applied to each tree in the expansion.
verbose	Verbose output. A logical vector indicating whether or not verbose output should be printed.

Value

A gradient tree boosting model. A gbm object.

autoMrP documentation built on Aug. 17, 2023, 5:07 p.m.