train_lightgbm: Boosted trees via lightgbm

In curso-r/treesnip: Tree-Based Model Engines for 'parsnip'

Boosted trees via lightgbm

Description

train_lightgbm is a wrapper for lightgbm tree-based models where all of the model arguments are in the main function.

Usage

train_lightgbm(
  x,
  y,
  max_depth = 17,
  num_iterations = 10,
  learning_rate = 0.1,
  feature_fraction = 1,
  min_data_in_leaf = 20,
  min_gain_to_split = 0,
  bagging_fraction = 1,
  quiet = FALSE,
  ...
)

Arguments

x	A data frame or matrix of predictors
y	A vector (factor or numeric) or matrix (numeric) of outcome data.
max_depth	An integer for the maximum depth of the tree.
num_iterations	An integer for the number of boosting iterations.
learning_rate	A numeric value between zero and one to control the learning rate.
feature_fraction	Subsampling proportion of columns.
min_data_in_leaf	A numeric value for the minimum sum of instances needed in a child to continue to split.
min_gain_to_split	A number for the minimum loss reduction required to make a further partition on a leaf node of the tree.
bagging_fraction	Subsampling proportion of rows.
quiet	A logical; should logging by lightgbm::lgb.train() be muted?
...	Other options to pass to lightgbm::lgb.train().

Value

A fitted lightgbm.Model object.

curso-r/treesnip documentation built on May 7, 2022, 1:10 a.m.