CODEWITHSUNDEEP
machine-learningweka
Evan Rosica
Evan Rosica

Reputation: 1242

Missing Values in WEKA output

I'm trying to compare J48 and MLP on a variety of datasets using WEKA. One of these is: https://archive.ics.uci.edu/ml/datasets/primary+tumor. I have converted this to CSV form which can be easily imported into WEKA. You can download this file here: https://ufile.io/8nj13

I used the "numeric to nominal" on the class and all the attributes to fit the natural structure of the data. However, when I ran J48 (and MLP), I got a bunch of question marks "?" in my output, presumably due to not having enough observations/instances of the appropriate type.

How can I get around this? I'm sure there must be a filter for this kind of thing. I've attached a picture below.

Missing output

Upvotes: 1

Views: 563

Answers (1)

K. Folkertsma
K. Folkertsma

Reputation: 51

The detailed accuracy table is displaying a question mark since no instance was actually classified as that specific class. This for example means that since no instance was classified as class 16, WEKA can not provide you with details regarding said class 16 classifications. This image might help you understand.

In regards to the amount of instances of the appropriate class, you can use the ClassBalancer filter under, found at weka/filters/supervised/instance/ClassBalancer. This should help balance out the amount of the various classes.

Also note that your dataset contains some missing values, this could be solved by either discarding the instances with missing data or running the ReplaceMissingValues filter, found at weka/filters/unsupervised/attribute/ReplaceMissingValues.

Upvotes: 1

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