Problem
Scenario
You're training a supervised learning model and have many candidate features, some redundant, some noisy, and some only weakly useful.
Question
What is your approach to feature selection in machine learning?
What this tests
- How you structure feature selection rather than naming one method
- Whether you distinguish filter, wrapper, and embedded approaches
- How you avoid leakage during selection
- How you validate that a smaller feature set still generalizes
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