Precision and Recall Quant Interview Guide
Precision and recall quant interview guide for definitions, threshold tradeoffs, rare events, classification examples, and metric choice.
Candidates evaluating rare-event models and screening decisions.
Precision asks how many flagged cases are real
Precision is the share of predicted positives that are true positives. It matters when acting on a positive prediction is costly or when too many false alarms waste effort.
Recall asks how many real cases you catch
Recall is the share of actual positives that the model finds. It matters when missing a positive case is expensive or when the screen is meant to be broad.
Concrete example
A model that flags potential signals may need high precision if each follow-up is expensive. A first-pass research screen may accept lower precision to avoid missing candidates.
Threshold choice controls the tradeoff
Raising the threshold often increases precision and lowers recall. Lowering the threshold often increases recall and lowers precision, though the exact movement depends on the score distribution.
Common mistakes
Candidates often say one metric is better in all cases. The useful answer chooses based on base rate, error cost, and what action follows a positive prediction.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.