HyperAI

True Positive

True ClassRefers to the samples that are correctly judged in the binary classification problem.

For the binary classification problem, samples can be divided into four categories according to the combination of their true categories and the categories predicted by the learner, namely, true positive, false positive, true negative and false negative.

True and False are used to judge whether the result is correct or not, Positive and Negative are used to judge whether it is positive or negative. Therefore, the total number of samples = TP + FP + TN + FN

The true class refers to the samples that were originally positive and were classified as positive samples. Taking finding apples in a pile of fruits as an example, TP refers to the apples that were found.

Related terms: false positive, true negative, false negative, ROC curve, AUC curve