TPR

True Positive Rate

Also known as sensitivity or recall, is a performance metric used to evaluate the effectiveness of a binary classification model. It measures the proportion of actual positive cases that the model correctly identifies.

The True Positive Rate is calculated as follows:

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Where:

• True Positives (TP) are the number of positive cases that the model correctly classified.
• False Negatives (FN) are the number of positive cases that were incorrectly classified as negative by the model.

A higher TPR indicates that the model is effectively identifying the positive cases in the dataset. However, it is essential to consider other metrics like specificity (true negative rate) and precision to get a more comprehensive understanding of the model’s performance, as a high false positive rate could accompany a high TPR.

• Abbreviation: TPR
• Source: What is Artificial Intelligence?