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Train a classifier and analyze dataset

OpenAIMachine Learning Engineer · Technical Screen
HardMachine Learning EngineerTechnical ScreenMachine Learning

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This question evaluates proficiency in end-to-end supervised machine learning workflows—covering data validation, exploratory data analysis, preprocessing, model training and selection, evaluation metrics and calibration, fairness assessments, and reproducibility—and tests competencies in applied machine learning engineering, statistical reasoning, and model governance within the Machine Learning domain. It is commonly asked in technical interviews because it verifies practical implementation skills and judgment for deploying reliable classifiers, aligning metrics to business goals and diagnosing performance and fairness across slices, with emphasis on practical application informed by conceptual understanding.

Given a labeled dataset, implement an end-to-end classifier training and dataset analysis workflow in Python. Perform exploratory data analysis (schema validation, missing values, target leakage checks, class imbalance, feature distributions). Create proper splits (stratified or time-based), define a baseline, and train at least two models (e.g., logistic regression and gradient boosting) with cross-validation and hyperparameter tuning. Handle imbalance (class weights or resampling), preprocessing (scaling, encoding), and calibration if needed. Choose metrics aligned with the business goal (ROC-AUC, PR-AUC, F1, cost-sensitive metrics), conduct error analysis (confusion slices, feature importances/SHAP), check fairness across key groups, and produce a concise report with the recommended model, expected performance, and code artifacts for reproducible deployment.