K-Fold Cross-Validation Visualization¶
About This MicroSim¶
This interactive visualization demonstrates how K-fold cross-validation works by showing how a dataset is partitioned into K equal-sized folds, with each fold taking turns as the validation set while the remaining folds form the training set.
How to Use¶
- Adjust K: Use the slider to change the number of folds (3-10)
- Next Fold: Step through each fold one at a time
- Run All Folds: Automatically animate through all K folds
- Reset: Return to the initial state
Key Concepts¶
- Training Folds (Blue): Data used to train the model in each iteration
- Validation Fold (Orange): Data used to evaluate the model in each iteration
- Cross-Validation Score: The mean accuracy across all K folds provides a more reliable performance estimate than a single train/validation split
Educational Value¶
This visualization helps students understand:
- How cross-validation ensures every data point is used for both training and validation
- Why averaging across multiple folds produces more reliable performance estimates
- The trade-off between K value and computational cost (higher K = more iterations)
- How cross-validation reduces the impact of lucky or unlucky single data splits
Learning Objectives¶
Bloom's Taxonomy Level: Understand (L2)
After using this MicroSim, students should be able to:
- Explain how K-fold cross-validation partitions a dataset
- Describe why cross-validation provides better performance estimates than a single split
- Understand the meaning of "K-fold" and how K affects the validation process
- Calculate the mean cross-validation score from individual fold results
Technical Details¶
- Library: p5.js
- Responsive: Yes (adapts to container width)
- Interactivity: Slider control, buttons for stepping/animation
- Data: Simulated accuracy values for demonstration
Integration¶
To embed this MicroSim in your course materials: