Improve Model Generalization with Cross-Validation and Feature Engineering
Company: Boston Consulting Group
Role: Data Scientist
Category: Machine Learning
Difficulty: medium
Interview Round: Take-home Project
##### 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.
Quick Answer: This question evaluates a data scientist's practical competence in supervised machine learning, covering stratified train/test splitting, reproducible preprocessing pipelines that standardize numeric features and robustly encode categoricals, training gradient-boosted models, and assessing discrimination with ROC AUC.