Cross-Validation Simulator

Visualize k-fold, stratified k-fold, and LOOCV to understand proper model validation

Overview

The Cross-Validation Simulator demonstrates different techniques for evaluating machine learning models. Compare holdout, k-fold, stratified k-fold, and leave-one-out cross-validation methods to understand how they split data and provide more reliable performance estimates than single train/test splits.

Tips

  1. Start with holdout validation to see its limitation - results vary significantly based on the random split
  2. Compare k-fold with different k values (k=3, 5, 10) to observe the trade-off between computational cost and estimate reliability
  3. Use stratified k-fold for classification problems especially with imbalanced data, to ensure each fold maintains the class distribution
  4. Try LOOCV on small datasets to see maximum data usage, but notice it becomes impractical with larger datasets
  5. Watch the variance across folds - smaller variance means more reliable performance estimates and better confidence in your model