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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D">Please define “sensibly”. I would be strongly opposed to modifying any models to incorporate “missingness”. No model handles missing data for you. That is for
you to decide based on your individual problem domain.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D">Take a look at a talk from last winter on missing data by Nina Zumel. Nina defines “sensibly” in several ways.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D"><a href="https://www.r-bloggers.com/prepping-data-for-analysis-using-r/">https://www.r-bloggers.com/prepping-data-for-analysis-using-r/</a><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:red;background:white">__________________________________________________________________________________________</span><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#212121"><br>
</span><b><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:#1F497D;background:white">Dale Smith</span></b><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:#1F497D;background:white"> | Macy's Systems and Technology | IFS
eCommerce | Data Science<br>
</span><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:#1F497D">770-658-5176 | 5985 State Bridge Road, Johns Creek, GA 30097 | dale.t.smith@macys.com<o:p></o:p></span></p>
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<p class="MsoNormal"><b><span style="font-size:10.0pt;font-family:"Tahoma","sans-serif"">From:</span></b><span style="font-size:10.0pt;font-family:"Tahoma","sans-serif""> scikit-learn [mailto:scikit-learn-bounces+dale.t.smith=macys.com@python.org]
<b>On Behalf Of </b>Stuart Reynolds<br>
<b>Sent:</b> Thursday, October 13, 2016 2:14 PM<br>
<b>To:</b> scikit-learn@python.org<br>
<b>Subject:</b> [scikit-learn] Missing data and decision trees<o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal"><span style="color:red">⚠ EXT MSG:</span> <o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:9.5pt">I'm looking for a decision tree and RF implementation that supports missing data (without imputation) -- ideally in Python, Java/Scala or C++.<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:9.5pt"><o:p> </o:p></span></p>
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<p class="MsoNormal"><span style="font-size:9.5pt">It seems that <span class="gmail-il">scikit</span>'s decision tree algorithm doesn't allow this -- which is disappointing because its one of the few methods that should be able to sensibly handle problems with
high amounts of missingness.<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:9.5pt"><o:p> </o:p></span></p>
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<p class="MsoNormal"><span style="font-size:9.5pt">Are there plans to allow missing data in <span class="gmail-il">scikit</span>'s decision trees?<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:9.5pt"><o:p> </o:p></span></p>
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<p class="MsoNormal"><span style="font-size:9.5pt">Also, is there any particular reason why missing values weren't supported originally (e.g. integrates poorly with other features)<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:9.5pt"><o:p> </o:p></span></p>
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<p class="MsoNormal"><span style="font-size:9.5pt">Regards<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:9.5pt">- Stuart<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="color:red">* This is an EXTERNAL EMAIL. Stop and think before clicking a link or opening attachments.</span>
<o:p></o:p></p>
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