setup_LightRF: Setup LightRF Hyperparameters
In egenn/rtemis: Machine Learning and Visualization

setup_LightRF

R Documentation

Setup LightRF Hyperparameters

Description

Setup hyperparameters for LightRF training.

Usage

setup_LightRF(
  nrounds = 500L,
  num_leaves = 4096L,
  max_depth = -1L,
  feature_fraction = 0.7,
  subsample = 0.623,
  lambda_l1 = 0,
  lambda_l2 = 0,
  max_cat_threshold = 32L,
  min_data_per_group = 32L,
  linear_tree = FALSE,
  ifw = FALSE,
  objective = NULL,
  device_type = "cpu",
  tree_learner = "serial",
  force_col_wise = TRUE,
  num_threads = 0L
)

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.
`feature_fraction`	(Tunable) Numeric: Fraction of features to use.
`subsample`	(Tunable) Numeric: Fraction of data to use.
`lambda_l1`	(Tunable) Numeric: L1 regularization.
`lambda_l2`	(Tunable) Numeric: L2 regularization.
`max_cat_threshold`	(Tunable) Positive integer: Maximum number of categories for categorical features.
`min_data_per_group`	(Tunable) Positive integer: Minimum number of observations per categorical group.
`linear_tree`	Logical: If TRUE, use linear trees.
`ifw`	Logical: If TRUE, use Inverse Frequency Weighting in classification.
`objective`	Character: Objective function.
`device_type`	Character: "cpu" or "gpu".
`tree_learner`	Character: "serial", "feature", "data", or "voting".
`force_col_wise`	Logical: Use only with CPU - If TRUE, force col-wise histogram building
`num_threads`	Integer: Number of threads to use. 0 means default number of threads in OpenMP.

Details

Get more information from lightgbm::lgb.train. Note that hyperparameters subsample_freq and early_stopping_rounds are fixed, and cannot be set because they are what makes lightgbm train a random forest. These can all be set when training gradient boosting with LightGBM.