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<meta content="text/html; charset=ISO-8859-1" http-equiv="Content-Type">
</head><body text="#000000" bgcolor="#FFFFFF">This is nothing to do with
Scikit guidelines criteria....<br>
<br>
This is about scientific/mathematic view Recurrent Decision Tree which
is a specific tree by nature<br>
(you cannot apply standard algos on this).<br>
<br>
Suppose very little number of people has experience with recurrence in
Decision Tree...<br>
<br>
<br>
<br>
<br>
<br>
<br>
<br>
<br>
<a class="moz-txt-link-abbreviated" href="mailto:scikit-learn-request@python.org">scikit-learn-request@python.org</a> wrote:
<blockquote
cite="mid:mailman.75.1478538005.20168.scikit-learn@python.org"
type="cite">
<pre wrap="">Send scikit-learn mailing list submissions to
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Today's Topics:
1. Re: Recurrent Decision Tree (Raghav R V)
----------------------------------------------------------------------
Message: 1
Date: Mon, 7 Nov 2016 15:51:11 +0100
From: Raghav R V <a class="moz-txt-link-rfc2396E" href="mailto:ragvrv@gmail.com"><ragvrv@gmail.com></a>
To: Scikit-learn user and developer mailing list
<a class="moz-txt-link-rfc2396E" href="mailto:scikit-learn@python.org"><scikit-learn@python.org></a>
Subject: Re: [scikit-learn] Recurrent Decision Tree
Message-ID:
<a class="moz-txt-link-rfc2396E" href="mailto:CACmxyDGsizwT3dW3h=nKuWSUQ1uJseLdraMPSP=4WrMvtHDhaA@mail.gmail.com"><CACmxyDGsizwT3dW3h=nKuWSUQ1uJseLdraMPSP=4WrMvtHDhaA@mail.gmail.com></a>
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Hi,
The reference paper seems pretty new with very few citations. Check our FAQ
on inclusion criterion -
<a class="moz-txt-link-freetext" href="http://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms">http://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms</a>
On Mon, Nov 7, 2016 at 2:10 PM, Dale T Smith <a class="moz-txt-link-rfc2396E" href="mailto:Dale.T.Smith@macys.com"><Dale.T.Smith@macys.com></a> wrote:
</pre>
<blockquote type="cite"><pre wrap="">Searching the mailing list would be the best way to find out this
information.
It may be in the contrib packages on github ? have you checked?
____________________________________________________________
____________________________________________________________
__________________
*Dale T. Smith* *|* Macy's Systems and Technology *|* IFS eCom CSE Data
Science
5985 State Bridge Road, Johns Creek, GA 30097 *|* <a class="moz-txt-link-abbreviated" href="mailto:dale.t.smith@macys.com">dale.t.smith@macys.com</a>
*From:* scikit-learn [<a class="moz-txt-link-freetext" href="mailto:scikit-learn-bounces+dale.t.smith=">mailto:scikit-learn-bounces+dale.t.smith=</a>
<a class="moz-txt-link-abbreviated" href="mailto:macys.com@python.org">macys.com@python.org</a>] *On Behalf Of *KevNo
*Sent:* Friday, November 4, 2016 4:44 PM
*To:* <a class="moz-txt-link-abbreviated" href="mailto:scikit-learn@python.org">scikit-learn@python.org</a>
*Subject:* [scikit-learn] Recurrent Decision Tree
? EXT MSG:
Just wondering if Recurrent Decision Tree has been investigated
by Scikit previously.
Main interest is in path dependant (time series data) problems,
the recurrence is often necessary to model the path dependent state.
In other words, wrong prediction will affect the subsequent predictions.
Here, a research paper on Recurrent Decision Tree,
from Walt Disney Research (!)
<a class="moz-txt-link-freetext" href="https://goo.gl/APGpvM">https://goo.gl/APGpvM</a>
Any thought is welcome.
Thanks
Brookm
<a class="moz-txt-link-abbreviated" href="mailto:scikit-learn-request@python.org">scikit-learn-request@python.org</a> wrote:
Send scikit-learn mailing list submissions to
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Today's Topics:
1. Re: hierarchical clustering (Gael Varoquaux)
2. Naive Bayes - Multinomial Naive Bayes tf-idf (Marcin Miro?czuk)
3. Re: hierarchical clustering (Jaime Lopez Carvajal)
4. Re: Naive Bayes - Multinomial Naive Bayes tf-idf (Andy)
----------------------------------------------------------------------
Message: 1
Date: Fri, 4 Nov 2016 10:36:49 +0100
From: Gael Varoquaux <a class="moz-txt-link-rfc2396E" href="mailto:gael.varoquaux@normalesup.org"><gael.varoquaux@normalesup.org></a> <a class="moz-txt-link-rfc2396E" href="mailto:gael.varoquaux@normalesup.org"><gael.varoquaux@normalesup.org></a>
To: Scikit-learn user and developer mailing list
<a class="moz-txt-link-rfc2396E" href="mailto:scikit-learn@python.org"><scikit-learn@python.org></a> <a class="moz-txt-link-rfc2396E" href="mailto:scikit-learn@python.org"><scikit-learn@python.org></a>
Subject: Re: [scikit-learn] hierarchical clustering
Message-ID: <a class="moz-txt-link-rfc2396E" href="mailto:20161104093649.GA137008@phare.normalesup.org"><20161104093649.GA137008@phare.normalesup.org></a> <a class="moz-txt-link-rfc2396E" href="mailto:20161104093649.GA137008@phare.normalesup.org"><20161104093649.GA137008@phare.normalesup.org></a>
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AgglomerativeClustering internally calls scikit learn's version of
cut_tree. I would be curious to know whether this is equivalent to
scipy's fcluster.
It differs in that it enable to add connectivity contraints.
------------------------------
Message: 2
Date: Fri, 4 Nov 2016 11:45:39 +0100
From: Marcin Miro?czuk <a class="moz-txt-link-rfc2396E" href="mailto:m.marcinmichal@gmail.com"><m.marcinmichal@gmail.com></a> <a class="moz-txt-link-rfc2396E" href="mailto:m.marcinmichal@gmail.com"><m.marcinmichal@gmail.com></a>
To: <a class="moz-txt-link-abbreviated" href="mailto:scikit-learn@python.org">scikit-learn@python.org</a>
Subject: [scikit-learn] Naive Bayes - Multinomial Naive Bayes tf-idf
Message-ID:
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Hi,
In our experiments, we use a Multinomial Naive Bayes (MNB). The traditional
MNB implies the TF weight of the words. We read in documentation
<a class="moz-txt-link-freetext" href="http://scikit-learn.org/stable/modules/naive_bayes.html">http://scikit-learn.org/stable/modules/naive_bayes.html</a> which describes
Multinomial Naive Bayes that "... where the data are typically represented
as word vector counts, although tf-idf vectors are also known to work well
in practice". The "word vector counts" is a TF and it is well known. We
have a problem which the "tf-idf vectors". In this case, i.e. tf-idf it
was implemented the approach of the D. M. Rennie et all Tackling the Poor
Assumptions of Naive Bayes Text Classification? In the documentation, there
are not any citation of this solution.
Best,
* This is an EXTERNAL EMAIL. Stop and think before clicking a link or
opening attachments.
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</pre></blockquote>
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</pre>
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