Discuss logistic regression limitations for PD

Quick Overview

Discuss logistic regression limitations for PD evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Discuss logistic regression limitations for PD

Company: Wells Fargo

Role: Data Scientist

Category: Machine Learning

Difficulty: medium

Interview Round: Technical Screen

What are the limitations of logistic regression for PD modeling? Consider assumptions (linearity in the log‑odds), interactions, multicollinearity, class imbalance, probability calibration, and interpretability versus flexibility.

Quick Answer: Discuss logistic regression limitations for PD evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Jul 26, 2025
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Discuss logistic regression limitations for PD

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:

  1. Assumption of linearity in the log-odds
  2. Interactions between predictors
  3. Multicollinearity among predictors
  4. Class imbalance and rare events
  5. Probability calibration
  6. Interpretability versus flexibility

Where relevant, note common mitigations used in practice.

Clarifying Questions to Ask Guidance

  • Clarify the task, data shape, labels, constraints, and evaluation metric.
  • State assumptions behind the math or modeling technique you choose.
  • Connect theory to practical training, debugging, and deployment implications.

What a Strong Answer Covers Guidance

  • Correct definitions and formulas where the prompt requires them.
  • A practical explanation of how the method behaves on real data.
  • Trade-offs, failure modes, diagnostics, and mitigation strategies.
  • Evaluation choices that match the product or modeling objective.

Follow-up Questions Guidance

  • How would noisy labels, class imbalance, or distribution shift affect the answer?
  • What would you monitor after deployment?
  • Which baseline would you compare against first?
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