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About this interview
A technical interview on Model Evaluation and Selection, pitched at the medium 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
Explain cross-validation strategies and articulate when to use each variant
Interpret ROC-AUC, precision-recall curves, and F1 score and state when each is preferred
Describe the impact of class imbalance on evaluation metrics and explain remedies
Explain hyperparameter tuning approaches including grid search, random search, and Bayesian optimization
Topics covered
Confusion matrix basicsPrecision and recallAccuracy limitationsF1 scoreROC-AUCK-fold cross-validationStratified cross-validationClass imbalance impact on accuracyClass imbalance and metric choicePR-AUC vs ROC-AUCClass imbalance remediesSMOTE and synthetic oversamplingLOOCV vs k-foldValidation vs test set
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“Can you walk me through what a confusion matrix is and what the four cells — true positives, false positives, true negatives, and false negatives — actually represent?
“What is SMOTE, how does it work under the hood, and what failure modes can it introduce?
“What is data leakage in the context of model evaluation, and can you give an example of how preprocessing steps like imputation or scaling can inadvertently introduce it?