False Positive Rate
False Positive Rate (FPR) is a statistical measure that represents the proportion of negative instances that are incorrectly classified as positive by a binary classification model. It is calculated as the ratio of false positives to the sum of true negatives and false positives. FPR is an important metric in evaluating the performance of a classification model, especially in situations where the cost of false positives is high. A high FPR indicates that the model is incorrectly classifying a large number of negative instances as positive, which can lead to unnecessary costs or negative consequences. In contrast, a low FPR indicates that the model is correctly identifying negative instances as negative, which is desirable in most applications.
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