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About this interview
A technical interview on Model Evaluation & Validation, pitched at the easy level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Define precision, recall, F1, ROC-AUC, and PR-AUC and explain when to prefer each
Explain the confusion matrix and the impact of class imbalance on metric choice
Describe cross-validation strategies including k-fold and stratified variants
Distinguish offline metrics from online business metrics and explain the gap between them
Topics covered
Confusion Matrix BasicsAccuracy and Class ImbalancePrecision and RecallF1 and F-beta ScoreROC-AUCPR-AUC vs ROC-AUCCross-ValidationStratified Cross-ValidationClass Imbalance and Metric ChoiceLearning CurvesMulti-Class MetricsRegression MetricsLog Loss / Cross-EntropyProbability Calibration
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“Walk me through the four cells of a confusion matrix — true positives, true negatives, false positives, and false negatives — and explain what each one means in plain terms.
“What is calibration in the context of a machine learning model, and how would you check whether the predicted probabilities coming out of a model are well calibrated?
“Explain the concept of a proper scoring rule. Why is log loss a proper scoring rule for probability forecasts, and what does that mean for how models are incentivized to behave?