tuning_spaces_rbv2: RandomBot Tuning Spaces

tuning_spaces_rbv2R Documentation

RandomBot Tuning Spaces

Description

Tuning spaces from the Kuehn (2018) article.

glmnet tuning space

  • alpha [0, 1]

  • s [1e-04, 1000]

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”, “partition”]

  • min.node.size [1, 100]

  • splitrule [“gini”, “extratrees”]

  • num.random.splits [1, 100]

mtry.power is replaced by mtry.ratio.

rpart tuning space

  • cp [1e-04, 1]

  • maxdepth [1, 30]

  • minbucket [1, 100]

  • minsplit [1, 100]

svm tuning space

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

  • cost [1e-04, 1000]

  • gamma [1e-04, 1000]

  • tolerance [1e-04, 2]

  • degree [2, 5]

xgboost tuning space

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

  • nrounds [2, 8]

  • eta [1e-04, 1]

  • gamma [1e-05, 7]

  • lambda [1e-04, 1000]

  • alpha [1e-04, 1000]

  • subsample [0.1, 1]

  • max_depth [1, 15]

  • min_child_weight [1, 100]

  • colsample_bytree [0.01, 1]

  • colsample_bylevel [0.01, 1]

  • rate_drop [0, 1]

  • skip_drop [0, 1]

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.


mlr3tuningspaces documentation built on April 12, 2022, 9:05 a.m.