1. What Is True Positive Rate?

True Positive Rate (TPR), also known as sensitivity or recall, measures the proportion of actual positives that are correctly identified by a test. It is a key metric in evaluating the performance of binary classification models.

2. How Does The Calculator Work?

The calculator uses the TPR formula:

Where:

• \( TP \) — True Positives: Correctly identified positive cases
• \( FN \) — False Negatives: Positive cases incorrectly identified as negative

Explanation: TPR ranges from 0 to 1, where 1 indicates perfect sensitivity (all actual positives are correctly identified).

3. Importance Of TPR Calculation

Details: TPR is crucial in medical testing, machine learning, and statistical analysis to evaluate how well a test identifies true positive cases. High TPR is particularly important in scenarios where missing a positive case has serious consequences.

4. Using The Calculator

Tips: Enter the number of true positives and false negatives as non-negative integers. The sum of TP and FN must be greater than zero for valid calculation.

5. Frequently Asked Questions (FAQ)

Q1: What is the difference between TPR and precision?
A: TPR measures the proportion of actual positives correctly identified, while precision measures the proportion of positive identifications that are actually correct.

Q2: What is a good TPR value?
A: The acceptable TPR value depends on the context. In medical screening, TPR > 0.9 is often desirable, but the optimal value varies by application.

Q3: How does TPR relate to F1 score?
A: F1 score is the harmonic mean of precision and TPR, providing a balanced measure of a test's accuracy.

Q4: Can TPR be 1?
A: Yes, when there are no false negatives (all actual positives are correctly identified), TPR equals 1.

Q5: What affects TPR in real-world applications?
A: Test threshold, population characteristics, and the prevalence of the condition can all affect the observed TPR value.