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What difficulty level is this interview question?

This is a easy difficulty Machine Learning question, commonly asked during Take-home Project rounds at Boston Consulting Group.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Boston Consulting Group during technical interviews.

Train GradientBoostingClassifier with 5-Fold Cross-Validation

Last updated: Mar 29, 2026

Quick Overview

This question evaluates proficiency in supervised machine learning model development and evaluation, including use of scikit-learn pipelines, GradientBoostingClassifier configuration, stratified 5-fold cross-validation, ROC-AUC performance measurement, and model serialization.

Boston Consulting Group

Aug 4, 2025, 10:55 AM

Data Scientist

Take-home Project

Machine Learning

Final Model Training: GradientBoostingClassifier with 5-Fold CV

Context

Assume the notebook already contains a prepared feature matrix X and a binary target y (0/1), with any necessary preprocessing completed. Your goal is to evaluate and train a final model.

Task

Build a scikit-learn Pipeline that uses a GradientBoostingClassifier.
Evaluate the model using 5-fold Stratified cross-validation and report the mean ROC-AUC.
Fit the model on the full dataset (X, y).
Save the trained pipeline to disk as model.pkl.

Deliverable

Provide the full Python code that performs all steps above.

Solution

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