Theory of Intelligence
MACHINE LEARNING•Supervised Learning•Regularized Linear Models•4.2 Lasso Regression

Introduction

L1 regularization pushes unimportant weights to exactly zero, giving sparse models that perform automatic feature selection.

Introduction

Ridge shrinks weights. Lasso goes further: it can push the weights of unhelpful features to exactly zero, removing those features from the model entirely.

Imagine packing for a trip with a strict weight limit:

  • Ridge says "take a smaller version of everything."
  • Lasso says "leave the things you don't really need at home."

That makes Lasso a built-in feature selector. Its name stands for Least Absolute Shrinkage and Selection Operator.

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