学习Weka - 精确和召回 - 维基示例到.Arff文件

Question

我是WEKA的新手和高级统计数据,从头开始了解WEKA措施.我已经完成了所有@ rushdi-shams示例,这些都是很好的资源.

在维基百科上,http://en.wikipedia.org/wiki/Precision_and_recall示例解释了一个简单的例子,关于在一组9只真正的狗和一些猫中检测7只狗的视频软件.我完全理解这个例子和召回计算.所以我的第一步,让我们看看Weka如何使用这些数据进行再现.我如何创建这样的.ARFF文件？有了这个文件我有一个错误的混淆矩阵,并且错误的精确度按类召回不是1,它应该是4/9(0.4444)

@relation 'dogs and cat detection'

@attribute              'realanimal'      {dog,cat}
@attribute              'detected'        {dog,cat}
@attribute              'class'           {correct,wrong}

@data
dog,dog,correct
dog,dog,correct
dog,dog,correct
dog,dog,correct
cat,dog,wrong
cat,dog,wrong
cat,dog,wrong
dog,?,?
dog,?,?
dog,?,?
dog,?,?
dog,?,?
cat,?,?
cat,?,?

输出Weka(不带过滤器)

===运行信息===

Scheme:weka.classifiers.rules.ZeroR 
Relation:     dogs and cat detection
Instances:    14
Attributes:   3
          realanimal
          detected
          class
Test mode:10-fold cross-validation

=== Classifier model (full training set) ===

ZeroR predicts class value: correct

Time taken to build model: 0 seconds

=== Stratified cross-validation ===
=== Summary ===

Correctly Classified Instances           4               57.1429 %
Incorrectly Classified Instances         3               42.8571 %
Kappa statistic                          0     
Mean absolute error                      0.5   
Root mean squared error                  0.5044
Relative absolute error                100      %
Root relative squared error            100      %
Total Number of Instances                7     
Ignored Class Unknown Instances          7     

=== Detailed Accuracy By Class ===

           TP Rate   FP Rate   Precision   Recall  F-Measure   ROC Area  Class
             1         1          0.571     1         0.727      0.65     correct
             0         0          0         0         0          0.136    wrong
Weighted Avg.    0.571     0.571      0.327     0.571     0.416      0.43 

=== Confusion Matrix ===

 a b   <-- classified as
 4 0 | a = correct
 3 0 | b = wrong

False Negative狗肯定有问题,或者我的ARFF方法完全错了,我还需要其他类型的属性吗？

谢谢

Answer 1

让我们从Precision和Recall的基本定义开始.

Precision = TP/(TP+FP)
Recall = TP/(TP+FN)

TP真阳性在哪里,FP是假阳性,FN是假阴性.

在上面的dog.arff文件中,Weka只考虑了前7个元组,忽略了剩下的7个.从上面的输出中可以看出它已经将所有7个元组分类为正确(4个正确的元组+ 3个错误的元组) ).

让我们计算正确和错误类的精度.首先是正确的课程:

Prec = 4/(4+3) = 0.571428571
Recall = 4/(4+0) = 1.

对于错误的班级:

Prec = 0/(0+0)= 0
recall =0/(0+3) = 0