Understanding Overfitting and Underfitting
This interactive visualization demonstrates three key concepts in machine learning:
- Underfitting: The model is too simple and fails to capture the underlying pattern in the data.
- Good Fit: The model captures the true pattern while being resistant to noise.
- Overfitting: The model is too complex and fits the noise in the training data.
How to Use:
Click the buttons above the visualization to switch between different fitting scenarios. The blue dots represent your training data points, while the colored lines show different model fits:
- Orange line: Underfitting (too simple)
- Green line: Good fit (balanced)
- Purple line: Overfitting (too complex)