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

categorical features increases.

1. The larger the majority class size, the smaller the cp value

2. The larger the minority class size, the larger the cp value should

3. The larger the number of features, the smaller the maxdepth value

4. The larger the number of numeric features, the larger the

5. The larger the number of categorical features, the smaller the

minbucket value should be.

minbucket value should be.

6. The larger the number of instances, the larger the minsplit value

1. For datasets with many numeric features, larger cost values and

smaller gamma values tend to be more effective.

2. For datasets with many categorical features, linear kernels tend

to be more effective.

3. For datasets with few numeric features, small cost values and

larger gamma values tend to be more effective.

4. For datasets with few categorical features, polynomial kernels

tend to be more effective.
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