Bias-Variance Tradeoff Demo
Overview
The Bias-Variance Tradeoff Demo provides an interactive visualization of one of machine learning’s fundamental concepts. Using polynomial regression, you can adjust model complexity and observe how it affects the balance between bias (underfitting) and variance (overfitting). The tool displays training and test error curves, helping you understand the sweet spot where models generalize best to unseen data.
Tips
- Start with low polynomial degrees (1-2) to see underfitting, then gradually increase to observe overfitting
- Watch the gap between training and test error as you increase complexity - a large gap indicates high variance
- The optimal model complexity is where test error is minimized, not where training error is lowest
- Use the bias-variance curve visualization to understand how both components change with model complexity
- Compare how different datasets (by regenerating data) show similar bias-variance patterns but different optimal complexities
- Remember that real-world model selection involves the same principles - simpler models when you have less data, more complex models when you have abundant data