Precision
Precision is a statistical metric that measures the proportion of true positive predictions among all positive predictions made by a model. In other words, it is the ability of a model to correctly identify the positive cases. Precision is calculated as the ratio of true positives to the sum of true positives and false positives. A high precision score indicates that the model is making very few false positive predictions. Precision is particularly important in situations where false positives are costly or dangerous, such as in medical diagnosis or fraud detection. However, a high precision score can sometimes come at the expense of recall, which measures the proportion of true positive predictions among all actual positive cases. Therefore, it is important to balance precision and recall when evaluating a model's performance.
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