seniorSupervised Learning
What is K-Fold Cross Validation and why is it more reliable than a single train-test split?
Updated May 17, 2026
Short answer
K-Fold Cross Validation splits data into K parts and trains/evaluates the model K times for a more stable performance estimate.
Deep explanation
K-Fold Cross Validation divides the dataset into K equal folds. In each iteration, one fold is used as validation and the remaining K-1 folds are used for training. This process repeats K times, and results are averaged. This reduces variance caused by random train-test splits and gives a more robust estimate of generalization performance.
Real-world example
Used in medical diagnosis models where dataset size is small and evaluation reliability is critical.
Common mistakes
- Using K-fold after test set evaluation or leaking test data into folds.
Follow-up questions
- What is LOOCV (Leave-One-Out Cross Validation)?
- When should you avoid K-Fold CV?