L1 vs L2 Regularization

Medium

machine-learning Google Netflix Two Sigma

Multiple Choice

L2 (Ridge) regularization, because it shrinks all coefficients equally L1 (Lasso) regularization, because it can drive coefficients to exactly zero Dropout regularization, because it randomly removes features during training No regularization is needed with only 500 features

Correct!

Incorrect — try again next time!

Upgrade to Pro to unlock solutions and explanations