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Default Tuning Spaces

Source: R/tuning_spaces_default.R
mlr_tuning_spaces_default.Rd

Tuning spaces from the Bischl (2023) article.

Source

Bischl B, Binder M, Lang M, Pielok T, Richter J, Coors S, Thomas J, Ullmann T, Becker M, Boulesteix A, Deng D, Lindauer M (2023). “Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges.”

glmnet tuning space

  • s \([1e-04, 10000]\) Logscale

  • alpha \([0, 1]\)

kknn tuning space

  • k \([1, 50]\) Logscale

  • distance \([1, 5]\)

  • kernel [“rectangular”, “optimal”, “epanechnikov”, “biweight”, “triweight”, “cos”, “inv”, “gaussian”, “rank”]

ranger tuning space

  • mtry.ratio \([0, 1]\)

  • replace [TRUE,FALSE]

  • sample.fraction \([0.1, 1]\)

  • num.trees \([1, 2000]\)

rpart tuning space

  • minsplit \([2, 128]\) Logscale

  • minbucket \([1, 64]\) Logscale

  • cp \([1e-04, 0.1]\) Logscale

svm tuning space

  • cost \([1e-04, 10000]\) Logscale

  • kernel [“polynomial”, “radial”, “sigmoid”, “linear”]

  • degree \([2, 5]\)

  • gamma \([1e-04, 10000]\) Logscale

xgboost tuning space

  • eta \([1e-04, 1]\) Logscale

  • nrounds \([1, 5000]\)

  • max_depth \([1, 20]\)

  • colsample_bytree \([0.1, 1]\)

  • colsample_bylevel \([0.1, 1]\)

  • lambda \([0.001, 1000]\) Logscale

  • alpha \([0.001, 1000]\) Logscale

  • subsample \([0.1, 1]\)