L1 Regularization (Lasso) for Feature Selection

L1 regularization, also called Lasso (Least Absolute Shrinkage and Selection Operator), adds a penalty proportional to the absolute value of coefficients to the loss function. This penalty forces some coefficients toward exactly zero, effectively removing less important features from the model. Unlike L2 regularization which shrinks coefficients gradually, L1 performs true feature elimination. This dual benefit—improved generalization and automatic feature selection—makes L1 particularly valuable when you have many features and suspect many are irrelevant. The sparsity it creates also improves model interpretability by keeping only the most impactful features.

Sign up free — one personalized lesson every day, matched to your role and goals.

Already have an account? Sign in