[scikit-learn] Recurrent Decision Tree

[Raghav R V]

Hi,

The reference paper seems pretty new with very few citations. Check our FAQ
on inclusion criterion -
http://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms


On Mon, Nov 7, 2016 at 2:10 PM, Dale T Smith <Dale.T.Smith at macys.com> wrote:

> 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 *|* dale.t.smith at macys.com
>
>
>
> *From:* scikit-learn [mailto:scikit-learn-bounces+dale.t.smith=
> macys.com at python.org] *On Behalf Of *KevNo
> *Sent:* Friday, November 4, 2016 4:44 PM
> *To:* scikit-learn at python.org
> *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 (!)
>
> https://goo.gl/APGpvM
>
>
> Any thought is welcome.
> Thanks
> Brookm
>
>
>
>
>
> scikit-learn-request at python.org wrote:
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> Today's Topics:
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>
>
>    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)
>
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> ----------------------------------------------------------------------
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>
>
> Message: 1
>
> Date: Fri, 4 Nov 2016 10:36:49 +0100
>
> From: Gael Varoquaux <gael.varoquaux at normalesup.org> <gael.varoquaux at normalesup.org>
>
> To: Scikit-learn user and developer mailing list
>
>         <scikit-learn at python.org> <scikit-learn at python.org>
>
> Subject: Re: [scikit-learn] hierarchical clustering
>
> Message-ID: <20161104093649.GA137008 at phare.normalesup.org> <20161104093649.GA137008 at phare.normalesup.org>
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> Content-Type: text/plain; charset=us-ascii
>
>
>
> 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 <m.marcinmichal at gmail.com> <m.marcinmichal at gmail.com>
>
> To: scikit-learn at python.org
>
> Subject: [scikit-learn] Naive Bayes - Multinomial Naive Bayes tf-idf
>
> Message-ID:
>
>         <CAH6=PuCebYLz32-YqpEUtRrYQvn7EQUiymWCy38Vi9_9Jr+-Fg at mail.gmail.com> <CAH6=PuCebYLz32-YqpEUtRrYQvn7EQUiymWCy38Vi9_9Jr+-Fg at mail.gmail.com>
>
> Content-Type: text/plain; charset="utf-8"
>
>
>
> 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
>
> http://scikit-learn.org/stable/modules/naive_bayes.html 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,
>
>
>
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-- 
Raghav RV
https://github.com/raghavrv
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