How does decision tree depth affect bias and variance?
Updated May 15, 2026
Short answer
Shallow trees have high bias and low variance, while deep trees have low bias and high variance.
Deep explanation
Decision tree depth controls model complexity. A shallow tree cannot capture complex relationships, leading to underfitting and high bias. A deep tree captures fine-grained patterns but may memorize training data, increasing variance. The optimal depth balances both using validation performance.
Real-world example
Credit scoring systems tune tree depth to avoid overfitting rare customer patterns.
Common mistakes
- Assuming deeper trees are always better.
Follow-up questions
- What is pruning in decision trees?
- How do you choose optimal tree depth?