Re: Assuring the completeness of training data set

>
>
>> > From the discussion which went on it looks like that I
>> should use smote or the filter suggested by wirefree (meant
>> for non-numeric classification?) . Both seem to add some
>> more instances. Can I do with the original dataset,
>>
>> sure you can. forget about the adding part for now.
>>
>> for original dataset I suggested running the following in
>> experimenter:
>> - iterations: 1
>> - folds: 10
>> - classifier selection: select e.g. the following
>> fast-running classifiers (doesn't matter much for our
>> purposes which we choose but best have several of them
>> though):
>>    bayes.NaiveBayes
>>    trees.J48
>>    lazy.IBk
>> (all classifiers in their default configurations)
>>
>> after it finishes, go to results tab and select from those
>> row/column buttons something reading "standard deviation"
>> -> results analysis: stdev could be e.g. +/- 2..3% which
>> under the above assumption of similarity between trainset
>> and testset is your figure of representativeness of your
>> trainset (the smaller this deviation is, the more
>> representative your trainset is)
>
> If I choose a different value in comparison field (in analyzer I get  
> different stddev in right window), like .04 and .1 for  
> Mean_absolute_error and Root_mean_squared_error respectively, shown  
> below:
>
> What should I choose there for analysis (in comparison field)?
> Harri, I am not clear with the method and concept regarding your  
> suggestion. Kindly explain.

>
> regards,
> Jitendra
> ============================================================
> Tester:     weka.experiment.PairedCorrectedTTester
> Analysing:  Mean_absolute_error
> Datasets:   1
> Resultsets: 1
> Confidence: 0.05 (two tailed)
> Sorted by:  -
> Date:       6/30/09 6:07 AM
>
>
> Dataset                   (1) functions.Linea
> ---------------------------------------------
> L2_MR                     (10)   0.64(0.04) |
> ---------------------------------------------
> (v/ /*)                                     |
>
>
> Key:
> (1) functions.LinearRegression '-S 0 -R 1.0E-8' -3364580862046573747
>
> ==============================================================
>
> Tester:     weka.experiment.PairedCorrectedTTester
> Analysing:  Root_mean_squared_error
> Datasets:   1
> Resultsets: 1
> Confidence: 0.05 (two tailed)
> Sorted by:  -
> Date:       6/30/09 6:08 AM
>
>
> Dataset                   (1) functions.Linea
> ---------------------------------------------
> L2_MR                     (10)   1.06(0.10) |
> ---------------------------------------------
> (v/ /*)                                     |
>
>
> Key:
> (1) functions.LinearRegression '-S 0 -R 1.0E-8' -3364580862046573747
> ======================================================================
>
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>