[scikit-learn] Recurrent Decision Tree

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

Re: [scikit-learn] Naive Bayes - Multinomial Naive Bayes tf-idf

On 11/04/2016 05:45 AM, Marcin Mirończuk wrote: 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 tha

Re: [scikit-learn] hierarchical clustering

Hi Roman, I will check that function too. Thanks for help. Have a good day, Jaime On Fri, Nov 4, 2016 at 4:28 AM, Roman Yurchak wrote: > Hi Jaime, > > Alternatively, in scikit learn I think, you could use >hac = AgglomerativeClustering(n_clusters, linkage="ward") >hac.fit(data) >cl

[scikit-learn] Naive Bayes - Multinomial Naive Bayes tf-idf

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

Re: [scikit-learn] hierarchical clustering

> 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. ___ scikit-learn mailing list sciki

Re: [scikit-learn] hierarchical clustering

Hi Jaime, Alternatively, in scikit learn I think, you could use hac = AgglomerativeClustering(n_clusters, linkage="ward") hac.fit(data) clusters = hac.labels_ there in an example on how to plot a dendrogram from this in https://github.com/scikit-learn/scikit-learn/pull/3464 Agglomerat