# Why using harmonic mean in F1 score

# Harmonic mean

Based on a nice explanation from [1]

The harmonic mean emphasizes the importance of smaller values;

in machine learning. The recall rate is R, and the accuracy rate is P. Using their evaluation of the algorithm, these two values usually restrict each other. In order to evaluate the quality of the algorithm more conveniently. So the F1 value was introduced.

F1 is the harmonic mean of the accuracy rate P and the recall rate R. Why does F1 use harmonic averages instead of numerical averages.

For example: when R is close to 1 and P is close to 0. The F1 value of the harmonic average is close to 0; and if the arithmetic average F1 is 0.5; obviously, the harmonic average can better evaluate the performance of the algorithm.Equivalent to evaluating the overall effect of R and P. Google translated from [1].

# Reference

[1]如何理解与应用调和平均数？ — 蓝绿黄红的回答 — 知乎 https://www.zhihu.com/question/23096098/answer/340657629