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You have 2,000 candidate features. How do you decide which ones to keep?

More 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.

Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.

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FEDITOR'S NOTE

The leakage point is what interviewers at Capital One and Amazon are really hunting for. A candidate who selects purely by importance score will happily keep a feature that is a proxy for the label and will not exist at scoring time. The senior move is to treat feature importance and predictive-of-the-label as a red flag to investigate, not a green light, and to check selection stability across resamples before trusting any ranking.

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