seniorSupervised Learning
What is hyperparameter tuning in supervised learning?
Updated May 17, 2026
Short answer
Hyperparameter tuning is the process of finding optimal model settings that are not learned from data.
Deep explanation
Hyperparameters like learning rate, depth of trees, or regularization strength control model behavior. Tuning methods include grid search, random search, and Bayesian optimization. Proper tuning improves generalization and reduces overfitting.
Real-world example
Optimizing deep learning models for image classification accuracy.
Common mistakes
- Tuning on test set instead of validation set.
Follow-up questions
- What is Bayesian optimization?
- Why is grid search expensive?