Re: Re: Re: Meaning of confusion matrix for OneR

>>> I applied OneR toFishers iris dataset with 10-fold cross-validation and minBucketSize = 3.  Petal Width was the attribute that OneR chose, with split at 0.8 and 1.65. The output below the rule says (144/150 instances correct).  When I look at the dataset, this checks out, with 4 virginica classified as versicolor, and 4 versicolor classified as virginica.  But the confusion matrix says that 6 virginica were classified as versicolor, and 6 versicolor as virginica.
>>>
>>> When I repeat this with minBucketSize = 6, the results are the same except the confusion matrix now says 2 virginica as versicolor and 7 versicolor as virginica.
>>>
>>> Why might this be?  Im using Weka 3.7.
>
>> 10-fold cross-validation generates 10 different models. The confusion
> matrix reflects this, the printed model is built on the full training
> data *before* cross-validation is performed (you can turn it off in
> the Explorer in the "More Options" dialog: "Output model"). This model
> is only printed to give the user an idea of what the classifier does
> on the data, it doesn't necessarily reflect the 10 CV models.
>
>> Cheers, Peter
>> --
>> Peter Reutemann, Dept. of Computer Science, University of Waikato, NZ
>> http://www.cs.waikato.ac.nz/~fracpete/           Ph. +64 (7) 858-5174
>
> *******************************
> Thanks, Peter.  Now for a follow-on question.  I modified the Fishers' iris dataset to have 4 missing values in each of the four attributes.  I used OneR with 10-fold cross validation and minBucketSize = 6.  The classifier model says:
>
> Petal Length:
> <  2.45 - setosa
> <  4.75 ->  versicolor
>> = 4.75 ->  virginica
> ? ->  virginica
>
> So, what does the last line mean?  Also, just to verify, is it true that OneR automatically replaces all missing values for an attribute the average value for the attribute?