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:[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]<mailto:[email protected]> wrote: Send scikit-learn mailing list submissions to [email protected]<mailto:[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]<mailto:[email protected]> You can reach the person managing the list at [email protected]<mailto:[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]><mailto:[email protected]> To: Scikit-learn user and developer mailing list <[email protected]><mailto:[email protected]> Subject: Re: [scikit-learn] hierarchical clustering Message-ID: <[email protected]><mailto:[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]><mailto:[email protected]> To: [email protected]<mailto:[email protected]> Subject: [scikit-learn] Naive Bayes - Multinomial Naive Bayes tf-idf Message-ID: <CAH6=pucebylz32-yqpeutrryqvn7equiymwcy38vi9_9jr+...@mail.gmail.com><mailto: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.
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