Interpretation of Naive Bayes results

Question

I am starting to use the NaiveBayes / Simple classifier to classify (Weka), however I have some problems that need to be understood when examining the data. The dataset I'm using is weather.nominal.arff.

While I am using the test use test from the parameters, the result of the classifier is:

Correctly Classified Instances 13  -  92.8571 %    
Incorrectly Classified Instances 1 - 7.1429 %   

a b classified as  
9 0  a =yes
1 4  b = no

My first question is, what should I understand from the wrong secret instances? Why did you get such a problem? Which attribute collection is incorrectly classified? is there any way to figure this out?

Secondly, when I try to do 10x cross-validation, why do I get different (less) correctly classified instances?

Results:

Correctly Classified Instances           8               57.1429 %
Incorrectly Classified Instances         6               42.8571 %

 a b   <-- classified as
 7 2 | a = yes
 4 1 | b = no

Answer 1

, :

... > > PlainText

:

=== Predictions on training set ===

 inst#     actual  predicted error prediction
     1       2:no       2:no       0.704 
     2       2:no       2:no       0.847 
     3      1:yes      1:yes       0.737 
     4      1:yes      1:yes       0.554 
     5      1:yes      1:yes       0.867 
     6       2:no      1:yes   +   0.737 
     7      1:yes      1:yes       0.913 
     8       2:no       2:no       0.588 
     9      1:yes      1:yes       0.786 
    10      1:yes      1:yes       0.845 
    11      1:yes      1:yes       0.568 
    12      1:yes      1:yes       0.667 
    13      1:yes      1:yes       0.925 
    14       2:no       2:no       0.652 

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