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Create Bayesian-Optimized AutoTuner for XGBoost

Source: R/BayesianTuner.R
make_autotuner_xgboost.Rd

Creates an AutoTuner with XGBoost learner and omics-optimized search space.

Usage

make_autotuner_xgboost(
  task,
  n_evals = NULL,
  inner_folds = 3,
  measure = "classif.auc",
  early_stopping = TRUE,
  nthread = 4L
)

Arguments

task

An mlr3 Task (used to set data-aware defaults)

n_evals

Number of Bayesian optimization iterations (default: auto based on n)

inner_folds

Number of inner CV folds for tuning (default: 3)

measure

Performance measure (default: classif.auc)

early_stopping

Use early stopping with nrounds (default: TRUE)

nthread

Number of threads for XGBoost (default: 4)

Value

An AutoTuner object ready for nested CV

Examples

if (FALSE) { # \dontrun{
if (requireNamespace("mlr3mbo", quietly = TRUE) &&
    requireNamespace("mlr3learners", quietly = TRUE) &&
    requireNamespace("xgboost", quietly = TRUE) &&
    requireNamespace("ranger", quietly = TRUE)) {
  at <- make_autotuner_xgboost(task, n_evals = 30)
  rr <- resample(task, at, rsmp("cv", folds = 5))
}
} # }