如何定义分类的混淆矩阵?

数据挖掘 混淆矩阵
2021-09-22 23:48:20

下面是使用两个标签(是和否)播放响应变量的数据集:

No. outlook temperature humidity    windy   play
1   sunny       hot     high        FALSE   no
2   sunny       hot     high        TRUE    no
3   overcast    hot     high        FALSE   yes
4   rainy       mild    high        FALSE   yes
5   rainy       cool    normal      FALSE   yes
6   rainy       cool    normal      TRUE    no
7   overcast    cool    normal      TRUE    yes
8   sunny       mild    high        FALSE   no
9   sunny       cool    normal      FALSE   yes
10  rainy       mild    normal      FALSE   yes
11  sunny       mild    normal      TRUE    yes
12  overcast    mild    high        TRUE    yes
13  overcast    hot     normal      FALSE   yes
14  rainy       mild    high        TRUE    no

以下是各自分类的决定:

1: (outlook,overcast) -> (play,yes) 
[Support=0.29 , Confidence=1.00 , Correctly Classify= 3, 7, 12, 13]

2: (humidity,normal), (windy,FALSE) -> (play,yes)
[Support=0.29 , Confidence=1.00 , Correctly Classify= 5, 9, 10]

3: (outlook,sunny), (humidity,high) -> (play,no) 
[Support=0.21 , Confidence=1.00 , Correctly Classify= 1, 2, 8]

4: (outlook,rainy), (windy,FALSE) -> (play,yes) 
[Support=0.21 , Confidence=1.00 , Correctly Classify= 4]

5: (outlook,sunny), (humidity,normal) -> (play,yes) 
[Support=0.14 , Confidence=1.00 , Correctly Classify= 11]

6: (outlook,rainy), (windy,TRUE) -> (play,no) 
[Support=0.14 , Confidence=1.00 , Correctly Classify= 6, 14]
1个回答

您只是在预测 Play = Yes 或 Play = No。

混淆矩阵如下所示:

             Predicted
          +------+------+
          |  Yes |  No  |
    +-------------------+
A   |     |      |      |
c   | Yes |  TP  |  FP  |
t   |     |      |      |
u   +-------------------+
a   |     |      |      |
l   | No  |  FN  |  TN  |
    |     |      |      |
    +-----+------+------+

TP: True positives
FP: False positives 
FN: False negatives 
TN: True negatives

然后可以将精度计算为 (TP + TN)/(TP + FP + TN + FN)。