Lasso

Also written: L1 regularization, L1 penalty

Regularization that adds the plain size of every weight to the loss. It pushes unhelpful weights to exactly zero, so it also picks features.

Other terms from the same lesson

• elastic net: Regularization that mixes the ridge and lasso penalties.
• regularization: Gently penalising a model for being too complicated, to stop it overfitting.
• ridge regression: Regularization that adds the squared size of every weight to the loss, so weights stay small but rarely reach exactly zero.
• weight decay: Shrinking every weight a tiny bit on each step, so the model keeps its weights small and is less likely to overfit.