53L1 vs L2 regularization: what does each do to the weights, why does L1 induce sparsity, and when would you prefer L1?▼mediumGoogleDatabricksScale1 replies◆ premiumL2 shrinks every weight smoothly toward zero; L1 drives many weights to exactly zero, which is automatic feature selection. The geometric reason L1 hits the corners is the part interviewers actually want, plus the call on when sparsity beats smooth shrinkage.Open full answer →
67You have 2,000 candidate features. How do you decide which ones to keep?▼mediumAmazonCapital OneGoogle1 replies◆ premiumMore features is not more signal, it is more variance, more leakage surface, and a thinner data manifold. The disciplined answer ranks filter, embedded, and wrapper methods by cost, leans on L1, and screens every survivor for leakage and stability.Open full answer →
