How would you build and evaluate a classifier?
Company: Microsoft
Role: Data Scientist
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
Overview: This question evaluates a data scientist's proficiency in binary classification model evaluation, end-to-end machine learning project design, and model interpretability, covering confusion matrix interpretation, the implications of Type I and Type II errors, trade-offs among metrics (accuracy, precision, recall, specificity, F1, ROC-AUC, PR-AUC, calibration), thresholding, deployment, monitoring, and SHAP-based explanations and their limitations. It is commonly asked in Machine Learning interviews for Data Scientist roles because it probes both conceptual understanding and practical application—assessing how metric choice and operational decisions align with business costs, class imbalance, and downstream actions.
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