Precision and recall are two important metrics used to evaluate the performance of classification models, particularly in settings where the classes are imbalanced or when the cost of false positives and false negatives differs significantly. ### Precision - **Definition**: Precision is the ratio of true positive predictions to the total number of positive predictions made by the model. It answers the question: "Of all the instances that were predicted as positive, how many were actually positive?
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