Learning Weka - Precision and Recall - Wiki example to .Arff file

Question

I'm new to WEKA and advanced statistics, starting from scratch to understand the WEKA measures. I've done all the @rushdi-shams examples, which are great resources.

On Wikipedia the http://en.wikipedia.org/wiki/Precision_and_recall examples explains with an simple example about a video software recognition of 7 dogs detection in a group of 9 real dogs and some cats. I perfectly understand the example, and the recall calculation. So my first step, let see in Weka how to reproduce with this data. How do I create such a .ARFF file? With this file I have a wrong Confusion Matrix, and the wrong Accuracy By Class Recall is not 1, it should be 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,?,?

Output Weka (without filters)

=== Run information ===

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

There must be something wrong with the False Negative dogs, or is my ARFF approach totally wrong and do I need another kind of attributes?

Thanks

Answer 1

Lets start with the basic definition of Precision and Recall.

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

Where TP is True Positive, FP is False Positive, and FN is False Negative.

In the above dog.arff file, Weka took into account only the first 7 tuples, it ignored the remaining 7. It can be seen from the above output that it has classified all the 7 tuples as correct(4 correct tuples + 3 wrong tuples).

Lets calculate the precision for correct and wrong class. First for the correct class:

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

For wrong class:

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