Space: 5527

Space: 5636

should be.

be.

should be.

should be.

Space: 5891

A.2 PD1

1. For datasets with more numeric features, smaller alpha and smaller
lambda values tend to be the best hyper-parameter configurations.

2. For datasets with more categorical features, larger alpha and

larger lambda values tend to be the best hyper-parameter
configurations.

3. For datasets with majority class size significantly larger than

minority class size, larger alpha and larger lambda values tend to be
the best hyper-parameter configurations.

1. The cost parameter tends to increase as the dataset size increases.
2. The gamma parameter tends to decrease as the number of numeric

features increases.

3. The kernel parameter tends to be radial for datasets with numeric
features, and polynomial or linear for datasets with categorical
features.

4. The degree parameter tends to increase as the number of

categorical features increases.
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