Important bug in percentage split for classification models
Jastrade <[email protected]> Fri, 30 Dec 2022 11:18:39 +0100
| Newsgroups | gmane.comp.ai.weka |
|---|---|
| Message-ID | <CAGNvjsf_pXM-3cGnshob_=pi58Vcge9Kijq9OL8WBTNrY3RYbQ@mail.gmail.com> |
Dear sirs, I am noticing something wrong in Weka version 3.8.6, that I think it is worth to describe and ask for explanation or solution if that is a bug. The example shown is with the classifier RepTree but it happens the same with the others. I am using percentage split 66%, and here you see the results [image: 2022-12-30_11h02_45.png] - It stays that the classification has been applied to the full training test, and it shows the results (for example the first) as: (1063/88)[528/53]. - Total of instances is 1063+528 = 1588. Then according to that, it splits the data in 66%/33% correctly. But look at what happens if I do the same but changing the pertentage split to 80% [image: 2022-12-30_11h07_14.png] Basically the results are exactly the same, and it also shows in the different rules, that it doesn´t split 80/20, but still the same 66/33 as before. So I have the following questions: 1. Does this mean that it is always splitting the 66/33 by defauld, whatever you specify? 2. Is it that maybe these rules are the result of using all data as training set in reality? I have seen if I press to More options, that I can click on “Output models for training splits”, and then it shows also other results, only using the % of data indicated in Pertentage split, but again, using those data, it shows results again splited 66/33%, as you see here: [image: 2022-12-30_11h09_57.png]ç [image: 2022-12-30_11h11_11.png] So it is really confusing for me what is this doing in reality… Can you please clarify? Thank you very much in advance, Best regards, Jastrade _______________________________________________ Wekalist mailing list -- [email protected] Send posts to [email protected] To unsubscribe send an email to [email protected] To subscribe, unsubscribe, etc., visit https://list.waikato.ac.nz/postorius/lists/wekalist.list.waikato.ac.nz List etiquette: http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html
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