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:
-
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.
Constraints & Assumptions
-
Preserve the scope, facts, inputs, and requested outputs from the prompt above.
-
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
-
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.
Clarifying Questions to Ask
-
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
-
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
-
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?