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关于第二章中的宏平均F1和微平均F1,我在周志华的机器学习一书中看到的计算是这样的,跟仓库中的介绍的有所区别。 Markdown格式的文本如下:
一种直接的做法是先在各混淆矩阵上分别计算出查准率和查全率, 记为 $\left(P_{1}, R_{1}\right),\left(P_{2}, R_{2}\right), \ldots,\left(P_{n}, R_{n}\right),$ 再计算平均值, 这样就得到“宏查准 率” $(\operatorname{macro}-P) 、 “{\text { 宏查全率” }(\operatorname{macro}-R), \text { 以及相应的“宏 } F 1 "(\operatorname{macro}-F 1):}$ $$ \begin{array}{c} \operatorname{macro}-P=\frac{1}{n} \sum_{i=1}^{n} P_{i} \ \operatorname{macro}-R=\frac{1}{n} \sum_{i=1}^{n} R_{i} \ \operatorname{macro}-F 1=\frac{2 \times \operatorname{macro}-P \times \operatorname{macro}-R}{\operatorname{macro}-P+\operatorname{macro}-R} \end{array} $$ 还可先将各混淆矩阵的对应元素进行平均, 得到 $T P 、 F P 、 T N 、 F N$ 的 平均值, 分别记为 $\overline{T P} 、 \overline{F P} 、 \overline{T N} 、 \overline{F N},$ 再基于这些平均值计算出“微查准 $$ \begin{array}{c} \operatorname{micro}-P=\frac{\overline{T P}}{\overline{T P}+\overline{F P}} \ \operatorname{micro}-R=\frac{\overline{T P}}{\overline{T P}+\overline{F N}} \ \operatorname{micro-} F 1=\frac{2 \times \operatorname{micro}-P \times \operatorname{micro}-R}{\operatorname{micro}-P+\operatorname{micro}-R} \end{array} $$
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关于第二章中的宏平均F1和微平均F1,我在周志华的机器学习一书中看到的计算是这样的,跟仓库中的介绍的有所区别。
![image-20201011182835191](https://github.com/camo/71da9a0675f797afa805c5e87df51c65bd9e20e3900f19974243437819e9d2aa/68747470733a2f2f67697465652e636f6d2f4c7557616e674c696e2f7069634265645f6e65772f7261772f6d61737465722f696d672f696d6167652d32303230313031313138323833353139312e706e67)
Markdown格式的文本如下:
The text was updated successfully, but these errors were encountered: