如何计算是、否类的Precision和Recall:
TP Rate FP Rate Precision Recall F-Measure ROC Area Class
0.444 0.8 0.5 0.444 0.471 0.522 yes
0.2 0.556 0.167 0.2 0.182 0.522 no
Weighted Avg. 0.357 0.713 0.381 0.357 0.367 0.522
,正确分类实例为35.714%
使用Weka的数据天气
@relation weather
@attribute outlook {sunny, overcast, rainy}
@attribute temperature real
@attribute humidity real
@attribute windy {TRUE, FALSE}
@attribute play {yes, no}
No. outlook temperature humidity windy play
1 sunny 85.0 85.0 FALSE no
2 sunny 80.0 90.0 TRUE no
3 overcast 83.0 86.0 FALSE yes
4 rainy 70.0 96.0 FALSE yes
5 rainy 68.0 80.0 FALSE yes
6 rainy 65.0 70.0 TRUE no
7 overcast 64.0 65.0 TRUE yes
8 sunny 72.0 95.0 FALSE no
9 sunny 69.0 70.0 FALSE yes
10 rainy 75.0 80.0 FALSE yes
11 sunny 75.0 70.0 TRUE yes
12 overcast 72.0 90.0 TRUE yes
13 overcast 81.0 75.0 FALSE yes
14 rainy 71.0 91.0 TRUE no
===========================
===运行信息===
Scheme:weka.classifiers.rules.PART -M 2 -C 0.25 -Q 1
Relation: weather
Instances: 14
Attributes: 5
outlook
temperature
humidity
windy
play
Test mode:10-fold cross-validation
=== Classifier model (full training set) ===
PART decision list
------------------
outlook = overcast: yes (4.0)
windy = TRUE: no (4.0/1.0)
outlook = sunny: no (3.0/1.0)
: yes (3.0)
Number of Rules : 4
Time taken to build model: 0 seconds
=== Stratified cross-validation ===
=== Summary ===
Correctly Classified Instances 5 35.7143 %
Incorrectly Classified Instances 9 64.2857 %
Kappa statistic -0.3404
Mean absolute error 0.5518
Root mean squared error 0.6935
Relative absolute error 115.875 %
Root relative squared error 140.5649 %
Total Number of Instances 14
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure ROC Area Class
0.444 0.8 0.5 0.444 0.471 0.522 yes
0.2 0.556 0.167 0.2 0.182 0.522 no
Weighted Avg. 0.357 0.713 0.381 0.357 0.367 0.522
=== Confusion Matrix ===
a b <-- classified as
4 5 | a = yes
4 1 | b = no
感谢并致以良好的问候
从混淆矩阵:
=== Confusion Matrix ===
a b <-- classified as
4 5 | a = yes
4 1 | b = no
Precision计算为4/8,即正确分类的a
的数量(yes)除以预测的a
的数量;Recall计算为4/9,即正确分类的a
的数量(yes)除以真实的a
的总数。另一个类的精度和召回率是相反的。
在一张小抄中查看所有这些标准的定义