Elastic Net: why blending L1 and L2 beats lasso on correlated features

Regularization is usually taught as a two-horse race: ridge or lasso, L2 or L1. But there is a case that trips up both, and it is extremely common in real data — features that are correlated with each other. I built an interactive demo where coordinate descent recomputes eight coefficients live as you drag two sliders, and it makes the failure of pure lasso, and the fix, impossible to miss. The setup that breaks lasso The demo dataset has eight standardized features. Two of them (x₁, x₂) are…