Feature Engineering & Selection Basics Questions
Understand why features matter and basic techniques: scaling/normalization, handling categorical variables (one-hot encoding, label encoding), creating interaction features, and feature importance. Know that good features are as important as good algorithms. Understand why feature scaling matters for algorithms like KNN or linear models.
EasyTechnical
0 practiced
Define interaction features and provide three examples where an interaction term is likely to improve predictive performance. Explain how you would test whether an interaction adds value without causing overfitting.
MediumTechnical
0 practiced
Describe common patterns of feature leakage during feature engineering. Given a dataset for churn prediction that includes a feature 'last_payment_date' and target 'churn_next_month', explain steps to detect if 'last_payment_date' leaks future information and how to fix it.
MediumTechnical
0 practiced
You find two features with Pearson correlation > 0.95. How do you decide which one to keep? Describe quantitative and qualitative approaches (e.g., VIF, model-based checks, domain knowledge) and the downstream implications for interpretability and model stability.
MediumTechnical
0 practiced
Given a dataset with column 'review_text' (customer reviews) and label 'helpful' (binary), describe a reproducible pipeline in Python to create basic text features: review length, average word length, count of exclamation/question marks, sentiment polarity score (via external lib), and TF-IDF limited to top 500 terms. Show pseudocode or concise code snippets for each step.
HardTechnical
0 practiced
You must process a high-throughput stream of categorical events and maintain approximate feature statistics for each category with limited memory. Propose algorithms/data structures (e.g., Welford for mean/variance, Count-Min Sketch, HyperLogLog) to compute running mean, variance, and approximate unique counts. Provide concise pseudocode for updates and how you'd use these summaries for online encodings.
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