setup.GBM: Set s_GBM parameters

In egenn/rtemis: Machine Learning and Visualization

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

egenn/rtemis documentation built on Dec. 17, 2024, 6:16 p.m.