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

Usage

cv_catboost(
  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_catboost(
  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
#>   iterations depth    metric
#> 1        100     3 0.7788221
#> 2        200     3 0.8007519
#> 3        100     5 0.7907268
#> 4        200     5 0.7719298