Supervised Learning Quant Interview Guide
Supervised learning quant interview guide for labels, losses, train/test splits, regression, classification, metrics, and pitfalls.
Candidates explaining regression, classification, and validation in market contexts.
Supervised learning starts with labels
A supervised model learns from input-output examples. In quant settings, the label definition, timing, and noise level often matter more than the algorithm.
Choose metrics that match the use
Regression may use mean squared error, rank correlation, or portfolio metrics. Classification may need precision, recall, calibration, or threshold-aware utility.
Concrete example
If predicting whether a stock outperforms tomorrow, accuracy can be misleading when classes are imbalanced or costs differ. Compare against simple baselines.
Split data honestly
Train/test splits should respect time, entities, and repeated observations. Random splits can leak information when rows share future-derived features or nearby timestamps.
Common mistakes
Candidates often talk about high accuracy without defining the label. Always connect the metric back to the decision the model is supposed to improve.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.