> For the complete documentation index, see [llms.txt](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/di-yi-zhou-ml-strategy/13-dan-yi-shu-zi-ping-gu-zhi-biao-ff08-single-number-evaluation-metric.md).

# 1.3 单一数字评估指标（Single number evaluation metric）

A和B模型的准确率（Precision）和召回率（Recall）分别如下：

![](/files/-Le0ctehu1wnp9KH2Nod)

使用单值评价指标F1 Score来评价模型的好坏。F1 Score综合了Precision和Recall的大小：

$$
F1=\frac{2\cdot P\cdot R}{P+R}
$$

![](/files/-Le0ctelpcunwweSaQG3)

还可以使用平均值作为单值评价指标：

![](/files/-Le0ctenHcgQQjIwyWHq)

> 不同国家样本的错误率，计算平均性能，选择平均错误率最小的模型（C模型）

![](/files/-Le0ctepKY_5eeNDBks_)![](/files/-Le0cterLbgNPEnNSc-T)