1. What is Specificity?

Specificity (also called true negative rate) measures the proportion of actual negatives that are correctly identified as such. It is a statistical measure of the performance of a binary classification test.

2. How Does the Calculator Work?

The calculator uses the specificity equation:

Where:

• \( TN \) — True negatives: cases correctly identified as negative
• \( FP \) — False positives: cases incorrectly identified as positive

Explanation: Specificity ranges from 0 to 1, where 1 indicates perfect specificity (no false positives).

3. Importance of Specificity

Details: Specificity is crucial when the cost of a false positive is high. It's especially important in medical testing where false positives can lead to unnecessary treatments.

4. Using the Calculator

Tips: Enter the count of true negatives and false positives from your confusion matrix. Both values must be non-negative integers.

5. Frequently Asked Questions (FAQ)

Q1: What's the difference between specificity and sensitivity?
A: Sensitivity measures true positive rate, while specificity measures true negative rate.

Q2: What is a good specificity value?
A: Values closer to 1 are better. 0.9+ is generally considered good, but depends on the application.

Q3: How does specificity relate to precision?
A: Specificity focuses on negative cases, while precision focuses on the accuracy of positive predictions.

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

Q5: When is high specificity important?
A: In situations where false positives are costly, like disease screening or spam filtering.