Axing a xgb.Booster.

xgb.Booster objects are created from the xgboost package, which provides efficient and scalable implementations of gradient boosted decision trees. Given the reliance of post processing functions on the model object, like xgb.Booster.complete, on the first class listed, the butcher_xgb.Booster class is not appended.

Usage

# S3 method for class 'xgb.Booster'
axe_call(x, verbose = FALSE, ...)

# S3 method for class 'xgb.Booster'
axe_env(x, verbose = FALSE, ...)

Arguments

x

A model object.

verbose

Print information each time an axe method is executed. Notes how much memory is released and what functions are disabled. Default is FALSE.

...

Any additional arguments related to axing.

Value

Axed xgb.Booster object.

Examples

library(xgboost)
library(parsnip)

data(agaricus.train)

if (utils::packageVersion("xgboost") > "2.0.0.0") {
  bst <- xgboost(x = agaricus.train$data,
                 y = as.factor(agaricus.train$label),
                 learning_rate = 1,
                 nthread = 2,
                 nrounds = 2,
                 eval_metric = "logloss",
                 objective = "binary:logistic")
} else {
  bst <- xgboost(data = agaricus.train$data,
                 label = agaricus.train$label,
                 eta = 1,
                 nthread = 2,
                 nrounds = 2,
                 eval_metric = "logloss",
                 objective = "binary:logistic",
                 verbose = 0)
}

out <- butcher(bst, verbose = TRUE)
#> ✖ No memory released. Do not butcher.

# Another xgboost model
fit <- boost_tree(mode = "classification", trees = 20) |>
  set_engine("xgboost", eval_metric = "mlogloss") |>
  fit(Species ~ ., data = iris)

out <- butcher(fit, verbose = TRUE)
#> ✖ The butchered object is 456 B larger than the original. Do not butcher.