mlr_tuning_spaces_rbv1 {mlr3tuningspaces}

RandomBot Tuning Spaces

Description

Tuning spaces from the Kuehn (2018) 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”]

• 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

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.