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Accuracy precision recall are key metrics used to evaluate the performance of models, particularly in classification tasks. Accuracy measures the proportion of correctly classified instances out of all predictions, reflecting overall correctness. Precision focuses on the accuracy of positive predictions, indicating how many identified positives are truly positive. Recall, also known as sensitivity, assesses how well the model captures all actual positives. Together, these metrics provide a comprehensive understanding of a model's performance, helping identify strengths and weaknesses. Balancing precision and recall is crucial for optimizing model effectiveness in real-world applications, especially where misclassifications have significant consequences.

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