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.
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
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
Compare feature hashing (the hashing trick) and learned embeddings for high-cardinality categorical variables. Discuss collision effects, interpretability, memory and compute trade-offs, and where each is typically preferred (e.g., linear models, tree models, deep learning). Give an example decision rule for choosing one over the other.
EasyTechnical
0 practiced
Write Python code using pandas to one-hot encode a categorical column 'color' in DataFrame 'df' and drop the first category to avoid multicollinearity (i.e., use drop='first'). Show how you would apply the same transformation to a separate test DataFrame 'df_test' while safely handling unseen categories.
HardTechnical
0 practiced
You're asked to convince a skeptical product manager to prioritize feature engineering over hyperparameter tuning for improving a model. Draft a concise pitch that includes: a data-driven example or hypothetical showing expected gains, an experiment plan (A/B or offline), required engineering effort and timeline, risk assessment, and how you'll measure ROI.
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