cv_catboost {stackgbm} | R Documentation |
catboost - parameter tuning and model selection with k-fold cross-validation and grid search
Description
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 |
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
[Package stackgbm version 0.1.0 Index]