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True positive rate (TPR): Difference between revisions
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{{see also|Machine learning terms}} | |||
==Introduction== | ==Introduction== | ||
[[True positive rate (TPR)]], also referred to as '''Sensitivity''', is a [[metric]] used to [[evaluate]] the performance of [[binary classification model]]s. TPR measures how many positive cases are correctly [[classified]] as such by the [[model]] out of all of the actual positives in the [[dataset]]. In other words, TPR is the percent of actual positives that are predicted as positive. | [[True positive rate (TPR)]], also referred to as '''Sensitivity''', is a [[metric]] used to [[evaluate]] the performance of [[binary classification model]]s. TPR measures how many positive cases are correctly [[classified]] as such by the [[model]] out of all of the actual positives in the [[dataset]]. In other words, TPR is the percent of actual positives that are predicted as positive. | ||
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Similar to a machine learning model, which attempts to identify all positive cases within a dataset. If it succeeds in finding all of them, we can say it did a good job and give it a high true positive rate; on the contrary, if some positive cases are missed, then its accuracy drops off and we attribute a low true positive rate. | Similar to a machine learning model, which attempts to identify all positive cases within a dataset. If it succeeds in finding all of them, we can say it did a good job and give it a high true positive rate; on the contrary, if some positive cases are missed, then its accuracy drops off and we attribute a low true positive rate. | ||
[[Category:Terms]] [[Category:Machine learning terms]] | |||