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False positive rate (FPR): Difference between revisions
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{{see also|Machine learning terms}} | |||
==Introduction== | ==Introduction== | ||
False positive rate (FPR) is the proportion of actual negative [[example]]s for which the model mistakenly predicted the positive [[class]]. [[Machine learning models]] use [[classification]] [[models]] to predict whether an [[input]] belongs to a certain [[class]] or not. Unfortunately, these predictions aren't always correct and sometimes the model may indicate that an input belongs in one class when it actually doesn't - this is known as a false positive and the rate at which false positives occur is known as the false positive rate (FPR). | False positive rate (FPR) is the proportion of actual negative [[example]]s for which the model mistakenly predicted the positive [[class]]. [[Machine learning models]] use [[classification]] [[models]] to predict whether an [[input]] belongs to a certain [[class]] or not. Unfortunately, these predictions aren't always correct and sometimes the model may indicate that an input belongs in one class when it actually doesn't - this is known as a false positive and the rate at which false positives occur is known as the false positive rate (FPR). | ||
==What is false positive rate (FPR)?== | ==What is false positive rate (FPR)?== | ||
False Positive Rate (FPR) is a performance [[metric]] used to gauge how often [[classification]] | False Positive Rate (FPR) is a performance [[metric]] used to gauge how often [[classification model]]s 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. | ||
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==Explain Like I'm 5 (ELI5)== | ==Explain Like I'm 5 (ELI5)== | ||
False positive rate is like when your teacher thinks you did your homework, but you actually didn't. The false positive rate is the number of times your teacher thinks you did your homework when you didn't, divided by the total number of times your teacher thinks you did your homework. A low false positive rate means your teacher doesn't think you did your homework very often when you didn't, which is good. But sometimes it's okay for your teacher to think you did your homework when you didn't, depending on why you didn't do it. | False positive rate is like when your teacher thinks you did your homework, but you actually didn't. The false positive rate is the number of times your teacher thinks you did your homework when you didn't, divided by the total number of times your teacher thinks you did your homework. A low false positive rate means your teacher doesn't think you did your homework very often when you didn't, which is good. But sometimes it's okay for your teacher to think you did your homework when you didn't, depending on why you didn't do it. | ||
[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]] | |||