What is Lasso Regression and why does it perform feature selection?

Lasso uses L1 penalty that forces some coefficients to exactly zero.

Lasso solves minimize RSS + λ||β||₁. The L1 constraint forms a diamond-shaped geometry, causing corners where coefficients become zero. This leads to sparse models and automatic feature selection, especially useful in high-dimensional data.