juniorDecision Trees
What is overfitting in Decision Trees?
Updated May 16, 2026
Short answer
Overfitting occurs when a model learns noise in training data instead of general patterns.
Deep explanation
Decision Trees are prone to overfitting because they can create very deep branches that perfectly classify training data but fail on unseen data. This reduces generalization performance.
Real-world example
A spam filter that memorizes specific emails instead of learning general spam patterns.
Common mistakes
- Assuming 100% training accuracy is desirable.
Follow-up questions
- How do you detect overfitting?
- How can you prevent it?