What are hyperparameters in Decision Trees?
Updated May 16, 2026
Short answer
Hyperparameters control the structure and behavior of a Decision Tree.
Deep explanation
Key hyperparameters include max_depth, min_samples_split, min_samples_leaf, and criterion. These parameters help control complexity and prevent overfitting.
Real-world example
Used in fraud detection systems to balance accuracy and interpretability.
Common mistakes
- Using default settings without tuning.
Follow-up questions
- What is max_depth?
- Why tune hyperparameters?