Create Bayesian-Optimized AutoTuner for XGBoost

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{
at <- make_autotuner_xgboost(task, n_evals = 30)
rr <- resample(task, at, rsmp("cv", folds = 5))
} # }