mlr3tuningspaces (version 0.5.2)

mlr_tuning_spaces_rbv1: 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