Given a dataset with 100 attributes, is it true that the
classification performance is more accurate on the testing dataset
if we use more attributes to split the dataset and why?
Please propose two methods to restrict the decision tree models in
order to avoid overfitting.
Given a dataset with 100 attributes, is it true that the classification performance is more accurate on the testing data
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