recall
Applied AI interview questions tagged recall, across every topic.
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Concepts behind "recall"
The curriculum that explains the ideas these questions test.
Foundational
Precision, Recall, and F1Precision is how many of your positive predictions were right; recall is how many of the actual positives you caught. They trade off as you move the decision threshold, and which matters depends on the cost of false positives vs false negatives. F1 is their harmonic mean. On imbalanced data, accuracy lies and these metrics (with PR-AUC) tell the truth. Applied-AI interviews probe them because choosing and tuning the threshold by business cost is a core, constantly-tested skill.📊 Evaluation & ML Foundations
Foundational
RAG EvaluationEvaluating a RAG system means evaluating retrieval and generation separately, because a bad answer is usually a retrieval failure (the right context was never fetched) and you cannot fix what you cannot localize. Retrieval is scored with recall@k (the ceiling for the whole system), precision, and rank metrics; generation is scored for faithfulness (is each claim supported by the context?) and answer quality. Applied-AI interviews probe it because measuring RAG end-to-end, and knowing which half failed, is the core debugging skill.📊 Evaluation & ML Foundations