Katkat
Back

Train GradientBoostingClassifier with 5-Fold Cross-Validation

Boston Consulting GroupData Scientist · Online Assessment
EasyData 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 Train GradientBoostingClassifier with 5-Fold Cross-Validation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Scenario

BCG CodeSignal notebook – final model training task

Question

Train a GradientBoostingClassifier on the prepared data using 5-fold cross-validation, report mean ROC-AUC, and save the trained model to disk (model.pkl). Provide the full Python code.

Hints

Pipeline ➜ cross_val_score ➜ joblib.dump.