mlr_tuning_spaces_rbv2 {mlr3tuningspaces}

RandomBot V2 Tuning Spaces

Description

Tuning spaces from the Binder (2020) article.

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”, “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] Logscale

• maxdepth [1, 30]

• minbucket [1, 100]

• minsplit [1, 100]

svm tuning space

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

• cost [1e-04, 1000] Logscale

• gamma [1e-04, 1000] Logscale

• tolerance [1e-04, 2] Logscale

• degree [2, 5]

xgboost tuning space

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

• nrounds [7, 2981] Logscale

• eta [1e-04, 1] Logscale

• gamma [1e-05, 7] Logscale

• lambda [1e-04, 1000] Logscale

• alpha [1e-04, 1000] Logscale

• subsample [0.1, 1]

• max_depth [1, 15]

• min_child_weight [1, 100] Logscale

• colsample_bytree [0.01, 1]

• colsample_bylevel [0.01, 1]

• rate_drop [0, 1]

• skip_drop [0, 1]

Source

Binder M, Pfisterer F, Bischl B (2020). “Collecting Empirical Data About Hyperparameters for Data Driven AutoML.” https://www.automl.org/wp-content/uploads/2020/07/AutoML_2020_paper_63.pdf.