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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D">Searching the mailing list would be the best way to find out this information.<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">It may be in the contrib packages on github – have you checked?<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>
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<p class="MsoNormal" align="center" style="text-align:center"><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 T. Smith</span></b><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:#1F497D;background:white">
</span><b><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:red;background:white">|</span></b><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:#1F497D;background:white"> Macy's Systems and Technology
</span><b><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:red;background:white">|</span></b><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:#1F497D;background:white"> IFS eCom CSE Data Science
<br>
</span><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:#1F497D">5985 State Bridge Road, Johns Creek, GA 30097 </span><b><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:red">|</span></b><span style="font-size:10.0pt;font-family:"Arial","sans-serif";color:#1F497D"> dale.t.smith@macys.com<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif";color:#1F497D"><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";color:windowtext">From:</span></b><span style="font-size:10.0pt;font-family:"Tahoma","sans-serif";color:windowtext"> scikit-learn [mailto:scikit-learn-bounces+dale.t.smith=macys.com@python.org]
<b>On Behalf Of </b>KevNo<br>
<b>Sent:</b> Friday, November 4, 2016 4:44 PM<br>
<b>To:</b> scikit-learn@python.org<br>
<b>Subject:</b> [scikit-learn] Recurrent Decision Tree<o:p></o:p></span></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">Just wondering if Recurrent Decision Tree has been investigated
<br>
by Scikit previously.<br>
<br>
Main interest is in path dependant (time series data) problems,<br>
the recurrence is often necessary to model the path dependent state.<br>
In other words, wrong prediction will affect the subsequent predictions.<br>
<br>
Here, a research paper on Recurrent Decision Tree, <br>
from Walt Disney Research (!)<br>
<br>
<span style="font-size:10.0pt;font-family:"Helvetica","sans-serif";color:#444444"><a href="https://goo.gl/APGpvM">https://goo.gl/APGpvM</a></span>
<br>
<br>
<br>
Any thought is welcome.<br>
Thanks<br>
Brookm<br>
<br>
<br>
<br>
<br>
<br>
<a href="mailto:scikit-learn-request@python.org">scikit-learn-request@python.org</a> wrote:
<o:p></o:p></p>
<pre>Send scikit-learn mailing list submissions to<o:p></o:p></pre>
<pre> <a href="mailto:scikit-learn@python.org">scikit-learn@python.org</a><o:p></o:p></pre>
<pre><o:p> </o:p></pre>
<pre>To subscribe or unsubscribe via the World Wide Web, visit<o:p></o:p></pre>
<pre> <a href="https://mail.python.org/mailman/listinfo/scikit-learn">https://mail.python.org/mailman/listinfo/scikit-learn</a><o:p></o:p></pre>
<pre>or, via email, send a message with subject or body 'help' to<o:p></o:p></pre>
<pre> <a href="mailto:scikit-learn-request@python.org">scikit-learn-request@python.org</a><o:p></o:p></pre>
<pre><o:p> </o:p></pre>
<pre>You can reach the person managing the list at<o:p></o:p></pre>
<pre> <a href="mailto:scikit-learn-owner@python.org">scikit-learn-owner@python.org</a><o:p></o:p></pre>
<pre><o:p> </o:p></pre>
<pre>When replying, please edit your Subject line so it is more specific<o:p></o:p></pre>
<pre>than "Re: Contents of scikit-learn digest..."<o:p></o:p></pre>
<pre><o:p> </o:p></pre>
<pre><o:p> </o:p></pre>
<pre>Today's Topics:<o:p></o:p></pre>
<pre><o:p> </o:p></pre>
<pre> 1. Re: hierarchical clustering (Gael Varoquaux)<o:p></o:p></pre>
<pre> 2. Naive Bayes - Multinomial Naive Bayes tf-idf (Marcin Miro?czuk)<o:p></o:p></pre>
<pre> 3. Re: hierarchical clustering (Jaime Lopez Carvajal)<o:p></o:p></pre>
<pre> 4. Re: Naive Bayes - Multinomial Naive Bayes tf-idf (Andy)<o:p></o:p></pre>
<pre><o:p> </o:p></pre>
<pre><o:p> </o:p></pre>
<pre>----------------------------------------------------------------------<o:p></o:p></pre>
<pre><o:p> </o:p></pre>
<pre>Message: 1<o:p></o:p></pre>
<pre>Date: Fri, 4 Nov 2016 10:36:49 +0100<o:p></o:p></pre>
<pre>From: Gael Varoquaux <a href="mailto:gael.varoquaux@normalesup.org"><gael.varoquaux@normalesup.org></a><o:p></o:p></pre>
<pre>To: Scikit-learn user and developer mailing list<o:p></o:p></pre>
<pre> <a href="mailto:scikit-learn@python.org"><scikit-learn@python.org></a><o:p></o:p></pre>
<pre>Subject: Re: [scikit-learn] hierarchical clustering<o:p></o:p></pre>
<pre>Message-ID: <a href="mailto:20161104093649.GA137008@phare.normalesup.org"><20161104093649.GA137008@phare.normalesup.org></a><o:p></o:p></pre>
<pre>Content-Type: text/plain; charset=us-ascii<o:p></o:p></pre>
<pre><o:p> </o:p></pre>
<blockquote style="margin-top:5.0pt;margin-bottom:5.0pt">
<pre>AgglomerativeClustering internally calls scikit learn's version of<o:p></o:p></pre>
<pre>cut_tree. I would be curious to know whether this is equivalent to<o:p></o:p></pre>
<pre>scipy's fcluster.<o:p></o:p></pre>
</blockquote>
<pre><o:p> </o:p></pre>
<pre>It differs in that it enable to add connectivity contraints.<o:p></o:p></pre>
<pre><o:p> </o:p></pre>
<pre><o:p> </o:p></pre>
<pre>------------------------------<o:p></o:p></pre>
<pre><o:p> </o:p></pre>
<pre>Message: 2<o:p></o:p></pre>
<pre>Date: Fri, 4 Nov 2016 11:45:39 +0100<o:p></o:p></pre>
<pre>From: Marcin Miro?czuk <a href="mailto:m.marcinmichal@gmail.com"><m.marcinmichal@gmail.com></a><o:p></o:p></pre>
<pre>To: <a href="mailto:scikit-learn@python.org">scikit-learn@python.org</a><o:p></o:p></pre>
<pre>Subject: [scikit-learn] Naive Bayes - Multinomial Naive Bayes tf-idf<o:p></o:p></pre>
<pre>Message-ID:<o:p></o:p></pre>
<pre> <a href="mailto:CAH6=PuCebYLz32-YqpEUtRrYQvn7EQUiymWCy38Vi9_9Jr+-Fg@mail.gmail.com"><CAH6=PuCebYLz32-YqpEUtRrYQvn7EQUiymWCy38Vi9_9Jr+-Fg@mail.gmail.com></a><o:p></o:p></pre>
<pre>Content-Type: text/plain; charset="utf-8"<o:p></o:p></pre>
<pre><o:p> </o:p></pre>
<pre>Hi,<o:p></o:p></pre>
<pre>In our experiments, we use a Multinomial Naive Bayes (MNB). The traditional<o:p></o:p></pre>
<pre>MNB implies the TF weight of the words. We read in documentation<o:p></o:p></pre>
<pre><a href="http://scikit-learn.org/stable/modules/naive_bayes.html">http://scikit-learn.org/stable/modules/naive_bayes.html</a> which describes<o:p></o:p></pre>
<pre>Multinomial Naive Bayes that "... where the data are typically represented<o:p></o:p></pre>
<pre>as word vector counts, although tf-idf vectors are also known to work well<o:p></o:p></pre>
<pre>in practice". The "word vector counts" is a TF and it is well known. We<o:p></o:p></pre>
<pre>have a problem which the "tf-idf vectors". In this case, i.e. tf-idf it<o:p></o:p></pre>
<pre>was implemented the approach of the D. M. Rennie et all Tackling the Poor<o:p></o:p></pre>
<pre>Assumptions of Naive Bayes Text Classification? In the documentation, there<o:p></o:p></pre>
<pre>are not any citation of this solution.<o:p></o:p></pre>
<pre><o:p> </o:p></pre>
<pre>Best,<o:p></o:p></pre>
<pre><o:p> </o:p></pre>
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