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RandomBot Tuning Spaces

Source: R/tuning_spaces_rbv1.R
mlr_tuning_spaces_rbv1.Rd

Tuning spaces from the Kuehn (2018) article.

Source

Kuehn D, Probst P, Thomas J, Bischl B (2018). “Automatic Exploration of Machine Learning Experiments on OpenML.” 1806.10961, https://arxiv.org/abs/1806.10961.

glmnet tuning space

  • alpha \([0, 1]\)

  • s \([1e-04, 1000]\) Logscale

kknn tuning space

  • k \([1, 30]\)

ranger tuning space

  • num.trees \([1, 2000]\)

  • replace [TRUE,FALSE]

  • sample.fraction \([0.1, 1]\)

  • mtry.ratio \([0, 1]\)

  • respect.unordered.factors [“ignore”, “order”]

  • min.node.size \([1, 100]\)

The tuning space of the ranger learner is slightly different from the original paper. The hyperparameter mtry.power is replaced by mtry.ratio and min.node.size is explored in a range from 1 to 100.

rpart tuning space

  • cp \([0, 1]\)

  • maxdepth \([1, 30]\)

  • minbucket \([1, 60]\)

  • minsplit \([1, 60]\)

svm tuning space

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

  • cost \([1e-04, 1000]\) Logscale

  • gamma \([1e-04, 1000]\) Logscale

  • degree \([2, 5]\)

xgboost tuning space

  • nrounds \([1, 5000]\)

  • eta \([1e-04, 1]\) Logscale

  • subsample \([0, 1]\)

  • booster [“gblinear”, “gbtree”, “dart”]

  • max_depth \([1, 15]\)

  • min_child_weight \([1, 100]\) Logscale

  • colsample_bytree \([0, 1]\)

  • colsample_bylevel \([0, 1]\)

  • lambda \([1e-04, 1000]\) Logscale

  • alpha \([1e-04, 1000]\) Logscale