Feature Engineering Interview Questions

Common feature engineering patterns

• Encoding categorical variables (one-hot, target, ordinal)
• Handling missing values (imputation strategies)
• Feature scaling and normalization
• Creating interaction and polynomial features
• Time-based feature extraction (lags, rolling windows)
• Feature selection using importance scores, correlation, or regularization

Common mistakes in feature engineering

• Using target encoding without proper cross-validation (data leakage)
• Dropping features with missing values instead of imputing
• Not considering feature interactions
• Applying feature engineering after train-test split incorrectly
• Over-engineering features without measuring their impact

How feature engineering is evaluated

Related ML concepts

• Model evaluation interview questions
• Deep learning interview questions