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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | |||
In [[machine learning]], [[true positive (TP)]] refers to [[example]]s that are correctly predicted as positive by a [[classification model]]. More precisely, true positives refer to examples that actually exist as positives in the [[test data set]] and were classified correctly by the [[model]] as such. True positives represent one of four possible outcomes from [[binary classification]] tasks - other options are [[true negative]], [[false positive]], and [[false negative]]. | In [[machine learning]], [[true positive (TP)]] refers to [[example]]s that are correctly predicted as positive by a [[classification model]]. More precisely, true positives refer to examples that actually exist as positives in the [[test data set]] and were classified correctly by the [[model]] as such. True positives represent one of four possible outcomes from [[binary classification]] tasks - other options are [[true negative]], [[false positive]], and [[false negative]]. | ||