<div dir="ltr"><div>Hi Andreas,</div><div><br></div><div>You are correct about weight of evidence. Information Value is a fancy term but it is very similar to mutual information. Also, this method is used most widely with uplift random forest methodology or any incremental modeling problems where the goal is to find subset of population who will contribute to ROI goal over the users who would have purchased it anyways and over the users who have negative effect because of promotion. </div><div><br></div><div>Citations for Information Value that I found - <a href="http://www.mwsug.org/proceedings/2013/AA/MWSUG-2013-AA14.pdf">http://www.mwsug.org/proceedings/2013/AA/MWSUG-2013-AA14.pdf</a></div><div><a href="http://documentation.statsoft.com/STATISTICAHelp.aspx?path=WeightofEvidence/WeightofEvidenceWoEIntroductoryOverview">http://documentation.statsoft.com/STATISTICAHelp.aspx?path=WeightofEvidence/WeightofEvidenceWoEIntroductoryOverview</a><br></div><div><br></div><div>More on Uplift Random Forest or Incremental Modeling - <a href="https://www.linkedin.com/pulse/need-more-lift-try-uplift-models-jeffrey-strickland-ph-d-cmsp">https://www.linkedin.com/pulse/need-more-lift-try-uplift-models-jeffrey-strickland-ph-d-cmsp</a></div><div><br></div><div>PS - The function I have has a special flag for uplift modeling. If this flag is set, then Information value and weight of evidence are calculated accordingly.</div><div><br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Oct 5, 2016 at 8:19 AM, Andreas Mueller <span dir="ltr"><<a href="mailto:t3kcit@gmail.com" target="_blank">t3kcit@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
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Hey Urvesh.<br>
That looks interesting. We recently added mutual information based
feature selection.<br>
To add this to scikit-learn, we would like to see that this is an
established method, for example via citations<br>
or forks or some other way.<br>
If it's only a year old (the date of the blog post) that might be a
bit fresh for us, and you<br>
can add it to scikit-learn contrib.<br>
<br>
We would also like to see that there are cases when it works better
than what is already established<br>
and what we have, like mutual info based selection.<br>
<br>
It looks like WOE is just the coefficient vector of Naive Bayes,
right?<br>
I don't quite understand the information value at a glance, though.<br>
<br>
Andy<div><div class="h5"><br>
<br>
<div class="m_-3377970145969045694moz-cite-prefix">On 10/04/2016 05:39 PM, urvesh patel
wrote:<br>
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<div class="gmail_extra">
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<div>I have been using R extensively until last few
months when I started using Python. I noticed that
Python doesn't have a function to compute information
value and weight of evidence. Detailed explanation - <a href="http://multithreaded.stitchfix.com/blog/2015/08/13/weight-of-evidence/" target="_blank">http://multithreaded.stitchf<wbr>ix.com/blog/2015/08/13/weight-<wbr>of-evidence/</a></div>
<div><br>
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<div>I have version 0 of this concept ready and I would
like to contribute to scikit-learn so that more and
more people can use it. What are the steps I need to
follow in order to do so ?<span class="m_-3377970145969045694HOEnZb"><font color="#888888"><br clear="all">
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-- <br>
<div class="m_-3377970145969045694m_-2392914490054261567gmail_signature">
<div><font face="Arial, sans-serif"><span style="line-height:18px">Thanking You,</span></font></div>
<div><font face="Arial, sans-serif"><span style="line-height:18px"><br>
</span></font></div>
<div><span style="line-height:18px"><font face="Arial, sans-serif">Urvesh Patel</font></span></div>
<div><span style="line-height:18px"><font face="Arial, sans-serif">Data Ninja</font></span></div>
<div><span style="line-height:18px"><font face="Arial, sans-serif">Udacity</font></span></div>
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</font></span></div>
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<br>
<div><br>
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<br>
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<br></blockquote></div><br><br clear="all"><div><br></div>-- <br><div class="gmail_signature" data-smartmail="gmail_signature"><div><font face="Arial, sans-serif"><span style="line-height:18px">Thanking You,</span></font></div><div><font face="Arial, sans-serif"><span style="line-height:18px"><br></span></font></div><div><span style="line-height:18px"><font face="Arial, sans-serif">Urvesh Patel</font><br><font face="tahoma, sans-serif">Columbia University<br><i>Masters in Operations Research</i></font></span></div></div>
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