[scikit-learn] Recurrent Decision Tree

[KevNo]

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>
> To: Scikit-learn user and developer mailing list
> 	<scikit-learn at python.org>
> Subject: Re: [scikit-learn] Recurrent Decision Tree
> Message-ID:
> 	<CACmxyDGsizwT3dW3h=nKuWSUQ1uJseLdraMPSP=4WrMvtHDhaA at mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> 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:
>>
>>
>>
>>     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<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>
>>
>> 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,
>>
>>
>>
>> * This is an EXTERNAL EMAIL. Stop and think before clicking a link or
>> opening attachments.
>>
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