What is max depth in Decision Trees and why is it important?
Updated May 16, 2026
Short answer
Max depth limits how deep a decision tree can grow to control overfitting.
Deep explanation
Max depth is a hyperparameter that restricts the number of levels in a decision tree. Deeper trees can model complex relationships but are prone to overfitting. Shallow trees generalize better but may underfit. Choosing optimal depth is a bias-variance tradeoff problem often solved using cross-validation.
Real-world example
In healthcare risk prediction, max depth is restricted to keep models interpretable for doctors.
Common mistakes
- Setting max_depth too high and assuming more complexity improves accuracy.
Follow-up questions
- How do you choose optimal max depth?
- What happens if max_depth is too small?
- Is max depth the only way to control overfitting?