1. What is the TPR Formula?

The TPR (True Positive Rate) formula, 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 classification models.

2. How Does the Calculator Work?

The calculator uses the TPR formula:

Where:

• \( TP \) — True Positives (number of correctly identified positive cases)
• \( FN \) — False Negatives (number of positive cases incorrectly identified as negative)

Explanation: The formula calculates the ratio of correctly identified positive cases to all actual positive cases.

3. Importance of TPR Calculation

Details: TPR is crucial in medical testing, machine learning, and statistics for evaluating how well a test or model identifies positive cases. High TPR indicates good sensitivity.

4. Using the Calculator

Tips: Enter the number of true positives and false negatives. Both values must be non-negative integers, and their sum must be greater than zero.

5. Frequently Asked Questions (FAQ)

Q1: What is the range of TPR values?
A: TPR ranges from 0 to 1, where 1 indicates perfect sensitivity (all positives correctly identified).

Q2: How does TPR relate to FPR?
A: TPR (True Positive Rate) measures sensitivity, while FPR (False Positive Rate) measures the proportion of negatives incorrectly identified as positive.

Q3: When is high TPR important?
A: High TPR is critical in medical screening tests where missing a positive case (false negative) could have serious consequences.

Q4: What is the difference between TPR and precision?
A: TPR measures how many actual positives were identified, while precision measures how many identified positives were actually correct.

Q5: Can TPR be 1 if there are no positives?
A: No, if there are no actual positive cases (TP + FN = 0), TPR is undefined as division by zero occurs.