Build and evaluate bad-link classifier

Q: Build and evaluate bad-link classifier

This is a Machine Learning interview question from Google for Data Scientist roles. View the full question and solution on PracHub.

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Question

You have 1,000 URLs labeled as bad or good and a much larger unlabeled pool, with bad links rare. Design features and train a logistic regression. Explain your evaluation plan under class imbalance: stratified K-folds, ROC-AUC vs PR-AUC, calibration (reliability curves), and why accuracy is misleading. Choose a decision threshold by minimizing expected misclassification cost given asymmetric costs. Discuss class weighting or resampling, leakage checks, monitoring for dataset shift between labeled and production traffic, and an offline-to-online validation plan with shadow or canary deployment.

Build and evaluate bad-link classifier

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