Answer / Harish Kumar Malik

Precision and recall are two important metrics for evaluating classification models. Precision is the proportion of true positives (correctly classified positive instances) among all predicted positives, while recall is the proportion of true positives among all actual positives in the data. In other words, precision measures how many of the predicted positives were actually correct, and recall measures how many of the actual positives were correctly identified.

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