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Improve Model Generalization with Cross-Validation and Feature Engineering

Boston Consulting GroupData Scientist · Online Assessment
MediumData ScientistOnline AssessmentMachine Learning

Last updated

This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Improve Model Generalization with Cross-Validation and Feature Engineering states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Scenario

Using the cleaned retail data, you must build a model to predict whether a customer will place an order next month.

Question

Split the prepared dataset into 80/20 train–test sets with stratification on the target variable. Standardize numeric features and one-hot encode categorical features in a reproducible pipeline. Train a gradient-boosted tree (e.g., XGBoost or LightGBM) and report AUC on the held-out test set. List two techniques you would use to improve the model’s generalization if AUC is low.

Hints

Demonstrate scikit-learn pipelines and proper evaluation.