setup.GBM: Set s_GBM parameters
In egenn/rtemis: Machine Learning and Visualization

setup.GBM

R Documentation

Set s_GBM parameters

Description

Set s_GBM parameters

Usage

setup.GBM(
  interaction.depth = 2,
  shrinkage = 0.001,
  max.trees = 5000,
  min.trees = 100,
  bag.fraction = 0.9,
  n.minobsinnode = 5,
  grid.resample.params = setup.resample("kfold", 5),
  ifw = TRUE,
  upsample = FALSE,
  downsample = FALSE,
  resample.seed = NULL,
  ...
)

Arguments

`interaction.depth`	[gS] Integer: Interaction depth.
`shrinkage`	[gS] Float: Shrinkage (learning rate).
`max.trees`	Integer: Maximum number of trees to fit
`min.trees`	Integer: Minimum number of trees to fit.
`bag.fraction`	[gS] Float (0, 1): Fraction of cases to use to train each tree. Helps avoid overfitting.
`n.minobsinnode`	[gS] Integer: Minimum number of observation allowed in node.
`grid.resample.params`	List: Output of setup.resample defining grid search parameters.
`ifw`	Logical: If TRUE, apply inverse frequency weighting (for Classification only). Note: If `weights` are provided, `ifw` is not used.
`upsample`	Logical: If TRUE, upsample cases to balance outcome classes (for Classification only) Note: upsample will randomly sample with replacement if the length of the majority class is more than double the length of the class you are upsampling, thereby introducing randomness
`downsample`	Logical: If TRUE, downsample majority class to match size of minority class
`resample.seed`	Integer: If provided, will be used to set the seed during upsampling. Default = NULL (random seed)
`...`	Additional arguments