juniorDecision Trees
What are splitting criteria in Decision Trees?
Updated May 16, 2026
Short answer
Splitting criteria determine how data is divided at each node, commonly using Gini impurity or entropy.
Deep explanation
Splitting criteria measure the quality of a split. Gini impurity measures misclassification probability, while entropy measures randomness. The algorithm chooses the split that minimizes impurity and maximizes information gain.
Real-world example
Used in customer churn prediction to split users based on usage behavior.
Common mistakes
- Confusing Gini and entropy as identical measures.
Follow-up questions
- What is information gain?
- Which is faster: Gini or entropy?