Instruction: Define feature engineering and provide examples of its impact on model performance.
Context: This question evaluates the candidate's knowledge of the process of preparing and manipulating data inputs for better model outcomes.
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The way I'd explain it in an interview is this: Feature engineering is the process of turning raw data into representations that make the signal more accessible to the model. That can mean creating aggregates, ratios, lags, text features, categorical encodings, interaction terms, or domain-specific transformations that better reflect the...
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