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 →
15What is point-in-time correctness, and how do you avoid leakage in continuous retraining?▼mediumCapital OneJPMorganUber1 replies○ sign inThe bug that makes offline metrics a lie: training on information that didn't exist at prediction time. A concrete fraud example with timestamps, the as-of join that fixes it, and the label-maturity trap automated retraining adds on top.Open full answer →
