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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 medium difficulty Machine Learning question, commonly asked during Technical Screen rounds at Citibank.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Citibank during technical interviews.

Discuss logistic regression limitations for PD

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of logistic regression assumptions and limitations for Probability of Default modeling — covering linearity in the log-odds, predictor interactions, multicollinearity, class imbalance and rare events, probability calibration, and the interpretability versus flexibility trade-off within the Machine Learning and credit risk modeling domain. It is commonly asked to assess a candidate's ability to recognize theoretical constraints and practical implications when choosing and validating risk models, testing both conceptual statistical understanding and practical application in real-world, imbalanced-data and regulatory contexts.

Citibank

Jul 26, 2025, 12:00 AM

Data Scientist

Technical Screen

Machine Learning

Limitations of Logistic Regression for PD (Probability of Default) Modeling

Context

You are building a credit risk Probability of Default (PD) model with logistic regression. Discuss its limitations and trade-offs.

Prompt

Explain the limitations of logistic regression for PD modeling, focusing on:

Assumption of linearity in the log-odds
Interactions between predictors
Multicollinearity among predictors
Class imbalance and rare events
Probability calibration
Interpretability versus flexibility

Where relevant, note common mitigations used in practice.

Solution

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