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 <[email protected]> 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 *|* [email protected] > > > > *From:* scikit-learn [mailto:scikit-learn-bounces+dale.t.smith= > [email protected]] *On Behalf Of *KevNo > *Sent:* Friday, November 4, 2016 4:44 PM > *To:* [email protected] > *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 > > > > > > [email protected] wrote: > > Send scikit-learn mailing list submissions to > > [email protected] > > > > To subscribe or unsubscribe via the World Wide Web, visit > > https://mail.python.org/mailman/listinfo/scikit-learn > > or, via email, send a message with subject or body 'help' to > > [email protected] > > > > You can reach the person managing the list at > > [email protected] > > > > When replying, please edit your Subject line so it is more specific > > than "Re: Contents of scikit-learn digest..." > > > > > > 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 <[email protected]> > <[email protected]> > > To: Scikit-learn user and developer mailing list > > <[email protected]> <[email protected]> > > Subject: Re: [scikit-learn] hierarchical clustering > > Message-ID: <[email protected]> > <[email protected]> > > 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 <[email protected]> <[email protected]> > > To: [email protected] > > Subject: [scikit-learn] Naive Bayes - Multinomial Naive Bayes tf-idf > > Message-ID: > > <CAH6=pucebylz32-yqpeutrryqvn7equiymwcy38vi9_9jr+...@mail.gmail.com> > <CAH6=pucebylz32-yqpeutrryqvn7equiymwcy38vi9_9jr+...@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. > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > > -- Raghav RV https://github.com/raghavrv
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