learning
/
DeepLearning-500-questions
mirror of https://github.com/scutan90/DeepLearning-500-questions.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #345 from CoderOverflow/patch-4 

2.16.16 直观理解AUC 内容修订
This commit is contained in:
scutan90 2019-04-13 14:04:14 +08:00 committed by GitHub
parent 11a6d75bcc de9338efd4
commit 631fd99d85
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 1 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
ch02_机器学习基础/第二章_机器学习基础.md
View File

@ -1240,7 +1240,7 @@ TPR = TP/(TP+FN) = 0.5； FPR = FP/(TN+FP) = 0；
​ 模型有很多评估方法，为什么还要使用ROC和AUC呢？
​ 因为ROC曲线有个很好的特性：当测试集中的正负样本的分布变换的时候，ROC曲线能够保持不变。在实际的数据集中经常会出现样本类不平衡，即正负样本比例差距较大，而且测试数据中的正负样本也可能随着时间变化。
### 2.16.17 直观理解AUC
### 2.16.16 直观理解AUC
​ 下图展现了三种AUC的值：
![](./img/ch2/2.40.15/1.png)