1. What is Precision?

Precision is a metric that quantifies the number of correct positive predictions made out of all positive predictions made. It's also called the positive predictive value.

2. How Does the Calculator Work?

The calculator uses the Precision formula:

Where:

• \( TP \) — True Positives: Correctly identified positive cases
• \( FP \) — False Positives: Incorrectly identified positive cases

Explanation: Precision measures how precise/accurate your model is when it predicts positive cases. A high precision means fewer false positives.

3. Importance of Precision

Details: Precision is crucial in scenarios where false positives are particularly costly or undesirable, such as spam detection (where you don't want legitimate emails marked as spam) or medical testing (where you don't want healthy patients diagnosed with a disease).

4. Using the Calculator

Tips: Enter the number of true positives (TP) and false positives (FP) from your confusion matrix. Both values must be non-negative integers.

5. Frequently Asked Questions (FAQ)

Q1: What's the difference between precision and recall?
A: Precision measures how many selected items are relevant, while recall measures how many relevant items are selected.

Q2: What is a good precision value?
A: Generally, values closer to 1 are better, but the acceptable range depends on your application. In some cases, 0.7 might be acceptable, while others require >0.95.

Q3: When should I prioritize precision?
A: Prioritize precision when the cost of false positives is high (e.g., spam detection, medical diagnosis).

Q4: Can precision be 1?
A: Yes, when there are no false positives (FP = 0), precision will be 1 (perfect precision).

Q5: What if both TP and FP are zero?
A: The calculator will return 0 in this case, as the equation becomes undefined (0/0).