The true positive rate (TPR) is a measure used in statistics, machine learning, and medical diagnostics to evaluate the performance of a binary classification model. It represents the proportion of actual positive cases that were correctly identified or classified as positive by the model. TPR is also known as sensitivity, recall, or hit rate.
True Positive Rate can be used to measure binary context problems. For example, predicting events, detecting diseases, quality control and ML – to evaluate the performance of classification algorithms or models.
TPR Rate measures the proportion of positive instances that are accurately detected as positive by the model. It is calculated as:
TPR = TP / (TP + FN)
Here’s a step-by-step guide to calculating the true positive rate:
The true positive rate (TPR) and false positive rate (FPR) are two important performance metrics used in binary classification problems to evaluate the effectiveness of a model.
If TPR measures the positive instances, FPR measures the proportion of negative instances that are incorrectly classified as positive by the model.
FPR is calculated as:
FPR = FP / (FP + TN)
An easier way to represent this is with a table:
True positive rate (TPR) is a performance metric used to evaluate the effectiveness of binary classification models in machine learning. In this case, it represents the proportion of positive instances that were correctly predicted as positive by the model.
