How to define confusion matrix for classification?
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16-10-2019 - |
문제
Below is the dataset where the response variable is play with two labels (yes, and no):
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
Here are the decisions with their respective classifications:
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]
해결책
You are just predicting if Play = Yes or Play = No.
The confusion matrix would look like this:
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
The accuracy can then be calculated as (TP + TN)/(TP + FP + TN + FN).
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