> 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/14-man-zu-he-you-hua-zhi-biao-ff08-satisficing-and-optimizing-metrics.md).

# 1.4 满足和优化指标（Satisficing and optimizing metrics）

当把所有的性能指标都综合在一起，构成单值评价指标比较困难时：可以把某些性能作为优化指标（Optimizing metic），寻求最优化值；而某些性能作为满意指标（Satisficing metic），只要满足阈值就行

![](/files/-Le0cvIlO8Pw5x-fHjSH)

Accuracy和Running time这两个性能不太合适综合成单值评价指标。可以将Accuracy作为优化指标（Optimizing metic），Running time作为满意指标（Satisficing metic）。给Running time设定一个阈值，在其满足阈值的情况下，选择Accuracy最大的模型。如果设定Running time必须在100ms以内，模型C不满足阈值条件，剔除；模型B相比较模型A而言，Accuracy更高，性能更好

如果要考虑N个指标，则选择一个指标为优化指标，其他N-1个指标都是满足指标：

$$
N\_{metric}:\left{ \begin{array}{l}
1\qquad \qquad \qquad Optimizing\ metric\\
N\_{metric}-1\qquad Satisificing\ metric
\end{array} \right.
$$

性能指标（Optimizing metic）需要优化，越优越好；满意指标（Satisficing metic）只要满足设定的阈值

![](/files/-Le0cvInla5wo0uqgURz)