True positive rate TPR is the ratio of the number of positive sample prediction results to the actual number of positive samples.

The concept of 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 significance of true case rate

The true positive rate is calculated as TPR = TP / (TP + FN)

It represents the proportion of the positive class rate in the correct detection. Putting TPR and FPR on the same graph can get the ROC curve, and the area under the ROC curve is AUC. ROC and AUC are usually used as performance metrics in model evaluation.

Related terms: False Positive Rate (FPR), ROC Curve, True Positive Class, True Negative Class