mlr_tuning_spaces_rbv2 {mlr3tuningspaces} | R Documentation |
RandomBot V2 Tuning Spaces
Description
Tuning spaces from the Binder (2020) article.
glmnet tuning space
alpha
s
Logscale
kknn tuning space
k
ranger tuning space
num.trees
replace [TRUE,FALSE]
sample.fraction
mtry.ratio
respect.unordered.factors [“ignore”, “order”, “partition”]
min.node.size
splitrule [“gini”, “extratrees”]
num.random.splits
mtry.power
is replaced by mtry.ratio
.
rpart tuning space
cp
Logscale
maxdepth
minbucket
minsplit
svm tuning space
kernel [“linear”, “polynomial”, “radial”]
cost
Logscale
gamma
Logscale
tolerance
Logscale
degree
xgboost tuning space
booster [“gblinear”, “gbtree”, “dart”]
nrounds
Logscale
eta
Logscale
gamma
Logscale
lambda
Logscale
alpha
Logscale
subsample
max_depth
min_child_weight
Logscale
colsample_bytree
colsample_bylevel
rate_drop
skip_drop
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.