## What is the F Score?

The F score, also called the F1 score or F measure, is a measure of a test’s accuracy. The F score is defined as the weighted harmonic mean of the test’s precision and recall. This score is calculated according to:

with the precision and recall of a test taken into account. Precision, also called the positive predictive value, is the proportion of positive results that truly are positive. Recall, also called sensitivity, is the ability of a test to correctly identify positive results to get the true positive rate. The F score reaches the best value, meaning perfect precision and recall, at a value of 1. The worst F score, which means lowest precision and lowest recall, would be a value of 0.

### Why is this Useful?

The F score is used to measure a test’s accuracy, and it balances the use of precision and recall to do it. The F score can provide a more realistic measure of a test’s performance by using both precision and recall. The F score is often used in information retrieval for measuring search, document classification, and query classification performance.

### Applications of the F Score

- Statistical Analysis
- Machine Learning
- Natural Language Processing Literature
- Information retrieval