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Build Classifier: Evaluate with AUROC for Imbalanced Data

GoogleData Scientist · Technical Screen
MediumData ScientistTechnical ScreenMachine Learning

Last updated

Evaluates classifier design for detecting dead links in imbalanced URL data with leakage-safe validation. Strong answers cover leakage-safe features, grouped validation, AUROC versus accuracy, PR metrics, thresholding, deployment, and monitoring.

Scenario

Detecting dead links with a data set of 1,000 labeled URLs (good vs. bad).

Question

Walk through how you would build and evaluate a classifier. Which metric would you choose and why might AUROC be preferred over accuracy for imbalanced data?

Hints

Logistic regression plus class-imbalance-aware metrics.