tl_fit_xgboost: Fit an XGBoost model

In tidylearn: A Unified Tidy Interface to R's Machine Learning Ecosystem

Fit an XGBoost model

Description

Fit an XGBoost model

Usage

tl_fit_xgboost(
  data,
  formula,
  is_classification = FALSE,
  nrounds = 100,
  max_depth = 6,
  eta = 0.3,
  subsample = 1,
  colsample_bytree = 1,
  min_child_weight = 1,
  gamma = 0,
  alpha = 0,
  lambda = 1,
  early_stopping_rounds = NULL,
  nthread = NULL,
  verbose = 0,
  ...
)

Arguments

data	A data frame containing the training data
formula	A formula specifying the model
is_classification	Logical indicating if this is a classification problem
nrounds	Number of boosting rounds (default: 100)
max_depth	Maximum depth of trees (default: 6)
eta	Learning rate (default: 0.3)
subsample	Subsample ratio of observations (default: 1)
colsample_bytree	Subsample ratio of columns (default: 1)
min_child_weight	Minimum sum of instance weight needed in a child (default: 1)
gamma	Minimum loss reduction to make a further partition (default: 0)
alpha	L1 regularization term (default: 0)
lambda	L2 regularization term (default: 1)
early_stopping_rounds	Early stopping rounds (default: NULL)
nthread	Number of threads (default: max available)
verbose	Verbose output (default: 0)
...	Additional arguments to pass to xgb.train()

Value

A fitted XGBoost model

tidylearn documentation built on Feb. 6, 2026, 5:07 p.m.