Tune XGBoost hyperparameters

Usage

tl_tune_xgboost(
  data,
  formula,
  is_classification = FALSE,
  param_grid = NULL,
  cv_folds = 5,
  early_stopping_rounds = 10,
  verbose = TRUE,
  ...
)

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
param_grid: Named list of parameter values to try
cv_folds: Number of cross-validation folds (default: 5)
early_stopping_rounds: Early stopping rounds (default: 10)
verbose: Logical indicating whether to print progress (default: TRUE)
...: Additional arguments

Value

A tidylearn_model object (the refit on full data using the best hyperparameters) with an attribute "tuning_results" containing a list with elements param_grid, results (per-combination CV output), best_params, best_iteration, best_score, and minimize.