Model stacking for boosted trees — stackgbm • stackgbm

Model stacking with a two-layer architecture: first layer being boosted tree models fitted by xgboost, lightgbm, and catboost; second layer being a logistic regression model.

Usage

stackgbm(x, y, params, n_folds = 5L, seed = 42, verbose = TRUE)

Arguments

x: Predictor matrix.
y: Response vector.
params: A list of optimal parameters for boosted tree models. Can be derived from cv_xgboost(), cv_lightgbm(), and cv_catboost().
n_folds: Number of folds. Default is 5.
seed: Random seed for reproducibility.
verbose: Show progress?

Value

Fitted boosted tree models and stacked tree model.

Examples

# check the vignette for code examples