D Building Surrogate Model for PD1

The metric in PD1 contains many NaN values, which correspond to network training divergence. For
benchmarking purposes, it is more important to be able to distinguish the top-performing solutions,
i.e., solutions above medium accuracy. To accomplish this, we adopt a two-stage surrogate approach.

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Prompt

Space description

Demonstrations

Here are some classiﬁcation datasets along with best hyper-parameter conﬁgurations to train a R
language model "Learner mlr.classif.svm from package(s) e1071" on them.

Dataset: The dataset name is "wilt". It contains 2 classes, 4839 instances, 6 features, 5 numeric
features, 1 categorical features. The majority class size is 4578 and the minority class size is 261.
Conﬁguration 1: cost is medium. gamma is large. kernel is radial.
Conﬁguration 2: cost is medium. gamma is medium. kernel is radial.
Conﬁguration 3: cost is large. gamma is medium. kernel is radial.
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