Lesson 4.3 Activity - Data Mining with WEKA course

[Aparna Mahadev]

I don't seem to get how to use valueindices to do the following: (Question 5)

... investigate the effect of the filter's valueIndices parameter. Use it to make a class value that is 1 only for Iris-versicolors. Run LinearRegression again, with the same settings.

Any help would be appreciated.  Thanks

[Peter Reutemann]

> I don't seem to get how to use valueindices to do the following: (Question
> 5)
> ... investigate the effect of the filter's valueIndices parameter. Use it to
> make a class value that is 1 only for Iris-versicolors. Run LinearRegression
> again, with the same settings.

"A filter that creates a new dataset with a boolean attribute
replacing a nominal attribute. In the new dataset, a value of 1 is
assigned to an instance that exhibits a particular range of attribute
values, a 0 to an instance that doesn't. The boolean attribute is
coded as numeric by default."

What is the label index of the Irisi-versicolor label?

Cheers, Peter
--
Peter Reutemann
Dept. of Computer Science
University of Waikato, NZ
+64 (7) 858-5174
http://www.cms.waikato.ac.nz/~fracpete/
http://www.data-mining.co.nz/

[Mahadev, Aparna]

Hi Peter:

I am doing the second course and I just took the midterm assessment - already submitted the answers but I need answer with explanation on the following question.  I am teaching Data Mining in the Fall 2016 semester, and I would like a clear explanation so I can understand the concepts enough to explain to my students.  This is the only question I got wrong.

Thanks in advance.  

Consider the following filtering configurations: 

supervised discretization combined with the filtered classifier

supervised discretization without the filtered classifier

equal-width discretization (10 bins) combined with the filtered classifier

equal-width discretization (10 bins) without the filtered classifier

equal-frequency discretization (10 bins) combined with the filtered classifier

equal-frequency discretization (10 bins) without the filtered classifier

Suppose you were to use each configuration on a particular dataset with a particular classifier, evaluated with cross-validation in the usual way. Which of the following best describes the performance you would expect? (> denotes better performance, ≈ denotes about the same, = denotes exactly the same).

 a ≈ b > c ≈ d > e ≈ f
 a ≈ b > e ≈ f > c ≈ d
 a > b > c = d > e ≈ f
 b > a > e ≈ f > c = d
 a = b > c = d > e ≈ f

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--

Dr. Aparna Mahadev
Professor, Computer Science Department
Worcester State University
486 Chandler Street, Worcester, MA 01602
508-929-8715
508-929-8156 (Fax)

[Peter Reutemann]

Answering the question of an exam in a public forum defeats the purpose of an exam, don't you think?

Cheers, Peter
--
Peter Reutemann
Dept. of Computer Science
University of Waikato, NZ
+64 (7) 858-5174
http://www.cms.waikato.ac.nz/~fracpete/
http://www.data-mining.co.nz

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[Mahadev, Aparna]

I didn't post it in the public forum at all.  I thought I was sending you a personal email. 

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