In PROC HPSPLIT for classification trees, which impurity measure is the default splitting criterion?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — Gini impurity is the default splitting criterion in PROC HPSPLIT for classification trees. It measures the probability that a randomly chosen element from a node would be incorrectly classified if labeled according to the node's class distribution.
Full Explanation
Gini impurity is correct because PROC HPSPLIT uses Gini impurity as its default splitting criterion for classification trees. The Gini impurity measures the probability that a randomly chosen element from the node would be incorrectly classified if randomly labeled according to the node distribution. Information gain (entropy reduction) is the criterion used by ID3/C4.5 algorithms and can be selected via CRITERION=ENTROPY in some SAS procedures. Chi-square statistic is used in CHAID-style trees. Variance reduction is used for regression trees.