# 1.10 理解人的表现（Understanding human-level performance）

医学图像识别的例子：

![](/files/-Le0capwYHlF-U6Z0wlF)

在减小误诊率的背景下，人类水平误差在这种情形下应定义为：0.5% error。但是实际应用中，不同人可能选择的human-level performance基准是不同的，这会带来一些影响

如果在为了部署系统或者做研究分析的背景下，也许超过一名普通医生即可，即人类水平误差在这种情形下应定义为：1% error

假如该模型training error为0.7%，dev error为0.8。如果选择Team of experienced doctors，即human-level error为0.5%，则bias比variance更加突出。如果选择Experienced doctor，即human-level error为0.7%，则variance更加突出。选择什么样的human-level error，有时候会影响bias和variance值的相对变化。当然这种情况一般只会在模型表现很好，接近bayes optimal error的时候出现。越接近bayes optimal error，模型越难继续优化，因为这时候的human-level performance可能是比较模糊难以准确定义的

![](/files/-Le0capym9c5XozmX_FA)

![](/files/-Le0caq-N85nbV4fZ2iJ)![](/files/-Le0caq1hRmedd8EE7e4)


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