Generate a parameter grid for cross-validation — cv_param

This function generates a parameter grid to be used in the cross-validation of gradient boosting decision tree (GBDT) models.

Usage

cv_param_grid(
  n_iterations = c(100, 200, 500, 1000),
  max_depth = c(3, 5, 7, 9),
  learning_rate = c(0.01, 0.05, 0.1, 0.2)
)

n_iterations: A numeric vector of the number of iterations (trees) for the GBDT model. This is equivalent to nrounds in XGBoost, num_iterations in LightGBM, and iterations in CatBoost.
max_depth: A numeric vector of the maximum tree depths. This parameter is equivalent to max_depth in XGBoost and LightGBM, and depth in CatBoost.
learning_rate: A numeric vector of learning rates for the GBDT model. This parameter is equivalent to eta in XGBoost, learning_rate in LightGBM, and ignored in CatBoost.

A list where the names are the parameter names and the values are vectors of possible values for those parameters.

params <- cv_param_grid(
  n_iterations = c(10, 100),
  max_depth = c(3, 5),
  learning_rate = c(0.01, 0.1)
)