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False positive rate (FPR): Difference between revisions
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False Positive Rate (FPR) is a performance [[metric]] used to gauge how often [[classification]] models incorrectly classify negative instances as positives. Put differently, FPR measures the proportion of negative instances incorrectly classified as positive by the model. Calculated by dividing total false positives by total true negatives and false positives. | False Positive Rate (FPR) is a performance [[metric]] used to gauge how often [[classification]] models incorrectly classify negative instances as positives. Put differently, FPR measures the proportion of negative instances incorrectly classified as positive by the model. Calculated by dividing total false positives by total true negatives and false positives. | ||
false positive rate = false | false positive rate = [[false positive]]s / (false positives + [[true negative]]s) | ||
False positives are errors that occur when a model incorrectly predicts an input belongs to a certain class when it does not. These false positives can have serious repercussions depending on the application of the model; for instance, in medical diagnosis they could prompt unnecessary surgeries or biopsies which are both invasive and expensive. | False positives are errors that occur when a model incorrectly predicts an input belongs to a certain class when it does not. These false positives can have serious repercussions depending on the application of the model; for instance, in medical diagnosis they could prompt unnecessary surgeries or biopsies which are both invasive and expensive. | ||
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==How is false positive rate calculated?== | ==How is false positive rate calculated?== | ||
False positive rate (FPR) is calculated as the number of false positives divided by the sum of true | False positive rate (FPR) is calculated as the number of [[false positives]] divided by the sum of [[true negative]]s and false positives, represented by this formula: | ||
FPR = (FP + TN), where FP represents the number of false positives and TN represents true negatives. False positives refer to instances that are predicted as positive by the model but are actually negative; on the other hand, true negatives refer to instances that were correctly predicted as negative by the model. | FPR = (FP + TN), where FP represents the number of false positives and TN represents true negatives. False positives refer to instances that are predicted as positive by the model but are actually negative; on the other hand, true negatives refer to instances that were correctly predicted as negative by the model. | ||