mlr_tuning_spaces_default {mlr3tuningspaces}

Default Tuning Spaces

Description

Tuning spaces from the Bischl (2021) article.

glmnet tuning space

• s [1e-04, 10000] Logscale

• alpha [0, 1]

kknn tuning space

• k [1, 50] Logscale

• distance [1, 5]

• kernel [“rectangular”, “optimal”, “epanechnikov”, “biweight”, “triweight”, “cos”, “inv”, “gaussian”, “rank”]

ranger tuning space

• mtry.ratio [0, 1]

• replace [TRUE,FALSE]

• sample.fraction [0.1, 1]

• num.trees [1, 2000]

rpart tuning space

• minsplit [2, 128] Logscale

• minbucket [1, 64] Logscale

• cp [1e-04, 0.1] Logscale

svm tuning space

• cost [1e-04, 10000] Logscale

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

• degree [2, 5]

• gamma [1e-04, 10000] Logscale

xgboost tuning space

• eta [1e-04, 1] Logscale

• nrounds [1, 5000]

• max_depth [1, 20]

• colsample_bytree [0.1, 1]

• colsample_bylevel [0.1, 1]

• lambda [0.001, 1000] Logscale

• alpha [0.001, 1000] Logscale

• subsample [0.1, 1]

Source

Bischl B, Binder M, Lang M, Pielok T, Richter J, Coors S, Thomas J, Ullmann T, Becker M, Boulesteix A, Deng D, Lindauer M (2021). “Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges.” 2107.05847, https://arxiv.org/abs/2107.05847.