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Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

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.

Interpret AUC Values and Handle Class Imbalance Techniques

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of the Area Under the ROC Curve (AUC) and its relationship to true positive rate and false positive rate, along with competency in handling severe class imbalance via approaches such as resampling, threshold tuning, and metric selection, testing both conceptual understanding of evaluation metrics and practical application of mitigation strategies. It is commonly asked in the Machine Learning domain to assess how well an interviewee can interpret classifier discrimination under skewed class distributions and choose appropriate evaluation and adjustment approaches during model development.

Boston Consulting Group

Aug 4, 2025, 10:55 AM

Data Scientist

Take-home Project

Machine Learning

AUC and Class Imbalance in Binary Classification

Context

You are evaluating a binary classifier using ROC–AUC and need to reason about performance under severe class imbalance.

Tasks

Define what the Area Under the ROC Curve (AUC) measures and how it relates to the True Positive Rate (TPR) and False Positive Rate (FPR).
Interpret models with:
- AUC = 0.5
- AUC = 0.9
List and briefly describe three techniques to handle severe class imbalance in binary classification, covering:
- Resampling
- Threshold tuning
- Metric selection

Solution

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