Decision Tree Splitting: Entropy vs. Misclassification Error

Why is entropy preferred over misclassification error to perform decision tree splitting?

The decision tree uses a top-down, greedy search approach with recursive partitioning. In the decision tree, the goal is to partition regions recursively until homogeneous clusters are formed. To make these partitions, a sufficient number of questions are asked.

To split the tree at each step, we need to choose the best attribute that maximizes the decrease in loss from parent to children node. Hence, …

cross-entropy-loss decision decision-tree entropy error machine learning tree