Logistic Regression Quant Interview Guide
Logistic regression quant interview guide for binary outcomes, log-odds intuition, probability outputs, thresholds, evaluation, and mistakes.
Candidates preparing for classification model questions.
Logistic regression models binary outcomes
Logistic regression is commonly used when the target is yes/no or event/no event. In interviews, explain the event definition, input features, and how scores become probabilities or decisions.
Coefficients live on log-odds
The model is linear in log-odds, not directly linear in probability. You do not need deep derivation for most interviews, but you should avoid interpreting coefficients as plain probability changes.
Concrete example
If a model estimates whether a trade will be profitable, the probability output still needs calibration checks, threshold choice, cost assumptions, and out-of-sample validation before it guides action.
Thresholds create tradeoffs
A probability score is not the same as a decision. Changing the threshold changes false positives, false negatives, precision, recall, and expected payoff.
Common mistakes
Candidates often evaluate logistic regression with accuracy alone. Stronger answers consider base rates, class imbalance, threshold costs, calibration, and validation.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.