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
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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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Message: 1
Date: Fri, 4 Nov 2016 10:36:49 +0100
From: Gael Varoquaux<[email protected]>
To: Scikit-learn user and developer mailing list
<[email protected]>
Subject: Re: [scikit-learn] hierarchical clustering
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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<[email protected]>
To: [email protected]
Subject: [scikit-learn] Naive Bayes - Multinomial Naive Bayes tf-idf
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<CAH6=pucebylz32-yqpeutrryqvn7equiymwcy38vi9_9jr+...@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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