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Interactive 3D/K-Fold Cross-Validation
Cross-Validation
Evaluate generalization across folds
Folds (k)
k = 5 folds5
210
Playback
Current fold1 / 5
Split Sizes
Train80
Test20
Accuracy
Mean78.47%
± Std4.13%
Fold 174.0%
Fold 274.1%
Fold 385.0%
Fold 480.2%
Fold 579.0%
CV estimate = mean ± std across folds. Smaller std means more stable model.

Stratified keeps class balance in each fold - always use for classification.

K-Fold Cross-Validation - Interactive Visualization

K-fold cross-validation splits your dataset into K equal folds, then trains and evaluates the model K times - each time using a different fold as the held-out test set. This gives K performance estimates whose average is far more reliable than any single train/test split. It is the standard way to detect overfitting and compare models fairly.

  • See the dataset divided into K color-coded folds and watch the training/validation assignment rotate
  • Understand why a single 80/20 split can be misleadingly optimistic or pessimistic depending on the data
  • Compare K=3, 5, 10, and leave-one-out - see how variance of the estimate changes with K
  • Learn stratified K-fold: ensuring class proportions are preserved in each fold
  • Understand cross-validation as the gold standard for hyperparameter tuning and model selection

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.