This question evaluates a candidate's mastery of core supervised learning concepts—bias–variance trade-off, overfitting versus underfitting and their mitigation, regularization (L1 vs L2), k-fold cross-validation, and linear regression assumptions—with emphasis on model generalization, evaluation, and statistical foundations.
You are in a rapid-fire onsite session with a CIO focused on machine-learning fundamentals for a Data Scientist role. Keep answers concise and use equations where helpful.
Be concise; include equations where appropriate.
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