cv_xgboost: xgboost - parameter tuning and model selection with k-fold...

In stackgbm: Stacked Gradient Boosting Machines

xgboost - parameter tuning and model selection with k-fold cross-validation and grid search

Description

xgboost - parameter tuning and model selection with k-fold cross-validation and grid search

Usage

cv_xgboost(
  x,
  y,
  params = cv_param_grid(),
  n_folds = 5,
  n_threads = 1,
  seed = 42,
  verbose = TRUE
)

Arguments

x	Predictor matrix.
y	Response vector.
params	Parameter grid generated by cv_param_grid().
n_folds	Number of folds. Default is 5.
n_threads	The number of parallel threads. For optimal speed, match this to the number of physical CPU cores, not threads. See respective model documentation for more details. Default is 1.
seed	Random seed for reproducibility.
verbose	Show progress?

Value

A data frame containing the complete tuning grid and the AUC values, with the best parameter combination and the highest AUC value.

Examples

sim_data <- msaenet::msaenet.sim.binomial(
  n = 100,
  p = 10,
  rho = 0.6,
  coef = rnorm(5, mean = 0, sd = 10),
  snr = 1,
  p.train = 0.8,
  seed = 42
)

params <- cv_xgboost(
  sim_data$x.tr,
  sim_data$y.tr,
  params = cv_param_grid(
    n_iterations = c(100, 200),
    max_depth = c(3, 5),
    learning_rate = c(0.1, 0.5)
  ),
  n_folds = 5,
  n_threads = 1,
  seed = 42,
  verbose = FALSE
)

params$df

stackgbm documentation built on May 29, 2024, 3:55 a.m.