Machine learning interviews reward methodology over algorithm knowledge. Expect questions on the bias-variance trade-off, why accuracy fails on imbalanced data, precision and recall trade-offs, cross-validation and why time series and grouped data need different splitting, regularisation, bagging versus boosting, and feature engineering. Data leakage and production monitoring are heavily probed, because they are where applied projects most often fail. The questions below cover the fundamentals and the deployment realities.
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