Precision and recall are two key metrics used to evaluate the performance of classification models, particularly in the context of fraud detection. Precision measures the accuracy of the positive predictions made by the model, while recall assesses how well the model identifies all relevant instances in the dataset. Both metrics help in understanding the balance between false positives and false negatives, which is crucial when aiming to minimize fraudulent activities without affecting legitimate transactions.
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