setup_LightRuleFit: Setup LightRuleFit Hyperparameters
In egenn/rtemis: Machine Learning and Visualization

setup_LightRuleFit

R Documentation

Setup LightRuleFit Hyperparameters

Description

Setup hyperparameters for LightRuleFit training.

Usage

setup_LightRuleFit(
  nrounds = 200L,
  num_leaves = 32L,
  max_depth = 4L,
  learning_rate = 0.1,
  subsample = 0.666,
  subsample_freq = 1L,
  lambda_l1 = 0,
  lambda_l2 = 0,
  objective = NULL,
  ifw_lightgbm = FALSE,
  alpha = 1,
  lambda = NULL,
  ifw_glmnet = FALSE,
  ifw = FALSE
)

Arguments

`nrounds`	(Tunable) Positive integer: Number of boosting rounds.
`num_leaves`	(Tunable) Positive integer: Maximum number of leaves in one tree.
`max_depth`	(Tunable) Integer: Maximum depth of trees.
`learning_rate`	(Tunable) Numeric: Learning rate.
`subsample`	(Tunable) Numeric: Fraction of data to use.
`subsample_freq`	(Tunable) Positive integer: Frequency of subsample.
`lambda_l1`	(Tunable) Numeric: L1 regularization.
`lambda_l2`	(Tunable) Numeric: L2 regularization.
`objective`	Character: Objective function.
`ifw_lightgbm`	(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in the LightGBM step.
`alpha`	(Tunable) Numeric: Alpha for GLMNET.
`lambda`	Numeric: Lambda for GLMNET.
`ifw_glmnet`	(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in the GLMNET step.
`ifw`	Logical: If TRUE, use Inverse Frequency Weighting in classification. This applies IFW to both LightGBM and GLMNET.