[scikit-learn] Recurrent Decision Tree

Jacob Schreiber jmschreiber91 at gmail.com
Mon Nov 7 13:08:51 EST 2016


It hasn't been investigated by the sklearn team to my knowledge. As Dale
said, there may be an independent implementation out there but not
officially related to sklearn.

On Mon, Nov 7, 2016 at 9:17 AM, KevNo <brookm291 at gmail.com> wrote:

> This is nothing to do with Scikit guidelines criteria....
>
> This is about scientific/mathematic view Recurrent Decision Tree which is
> a specific tree by nature
> (you cannot apply standard algos on this).
>
> Suppose very little number of people has experience with recurrence in
> Decision Tree...
>
>
>
>
>
>
>
>
> scikit-learn-request at python.org wrote:
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> Today's Topics:
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>    1. Re: Recurrent Decision Tree (Raghav R V)
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>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Mon, 7 Nov 2016 15:51:11 +0100
> From: Raghav R V <ragvrv at gmail.com> <ragvrv at gmail.com>
> To: Scikit-learn user and developer mailing list
> 	<scikit-learn at python.org> <scikit-learn at python.org>
> Subject: Re: [scikit-learn] Recurrent Decision Tree
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>
> 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> <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= <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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> When replying, please edit your Subject line so it is more specific
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> than "Re: Contents of scikit-learn digest..."
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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)
>
>
>
>
>
> ------------------------------------------------------------
> ----------
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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> <gael.varoquaux at normalesup.org> <gael.varoquaux at normalesup.org>
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> Subject: Re: [scikit-learn] hierarchical clustering
>
> Message-ID: <20161104093649.GA137008 at phare.normalesup.org> <20161104093649.GA137008 at phare.normalesup.org> <20161104093649.GA137008 at phare.normalesup.org> <20161104093649.GA137008 at phare.normalesup.org>
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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 <m.marcinmichal at gmail.com> <m.marcinmichal at gmail.com> <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> <CAH6=PuCebYLz32-YqpEUtRrYQvn7EQUiymWCy38Vi9_9Jr+-Fg at mail.gmail.com> <CAH6=PuCebYLz32-YqpEUtRrYQvn7EQUiymWCy38Vi9_9Jr+-Fg at mail.gmail.com>
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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
> 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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