mlr_tuning_spaces_rbv2 | R Documentation |
Tuning spaces from the Binder (2020) article.
alpha [0, 1]
s [1e-04, 1000]
Logscale
k [1, 30]
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
.
cp [1e-04, 1]
Logscale
maxdepth [1, 30]
minbucket [1, 100]
minsplit [1, 100]
kernel [“linear”, “polynomial”, “radial”]
cost [1e-04, 1000]
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gamma [1e-04, 1000]
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tolerance [1e-04, 2]
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degree [2, 5]
booster [“gblinear”, “gbtree”, “dart”]
nrounds [7, 2981]
Logscale
eta [1e-04, 1]
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gamma [1e-05, 7]
Logscale
lambda [1e-04, 1000]
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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]
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.
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