Specificity

In data science, specificity refers to the ability of a model to correctly identify negative cases. It is the proportion of true negatives out of all negative cases. Specificity is an important metric in evaluating the performance of binary classification models, especially in imbalanced datasets where the number of negative cases is much larger than the number of positive cases. A high specificity indicates that the model is good at identifying negative cases and has a low false positive rate. On the other hand, a low specificity indicates that the model is not good at identifying negative cases and has a high false positive rate.
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