Regression Explorer - Interactive Visualization

OLS minimizes sum of squared residuals. Ridge adds L2 penalty λ·||β||². Lasso adds L1 penalty λ·||β||₁. This visualization fits all three to the same data simultaneously, showing how regularization shrinks coefficients and reduces overfitting. Dragging data points shows sensitivity; adjusting λ shows the bias-variance tradeoff.

• Drag data points and watch all three regression lines update
• Adjust λ slider to see Ridge shrink coefficients, Lasso zero them out
• See residuals as vertical dashed lines from each point to fit
• Compare R², RSS for OLS vs Ridge vs Lasso
• Understand how regularization trades bias for reduced variance

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.