[scikit-learn] Machine learning for PU data

Hi All, I am a scikit-learn user and have a question for the community, if anyone has applied any available machine learning algorithms in the scikit-learn package for data with positive and unlabeled class only? If so would you share some insight with me. I understand this could be a broader topi

Re: [scikit-learn] Machine learning for PU data

Hello Ruchika, I don't think that scikit-learn currently has algorithms that can train with positive and unlabeled class labels only. However, you could try one of the following compatible wrappers, - http://nktmemo.github.io/jekyll/update/2015/11/07/pu_classification.html - https://githu

[scikit-learn] Construct the microclusters using a CF-Tree

Hi all, I want to ask you about clustering usign Birch clustering algorithm. I have a *distance matrix* n*n M where M_ij is the distance between object_i and object_j.(You can see file format in the attachment). I want to cluster them using Birch clustering algorithm. Does this method have 'preco

Re: [scikit-learn] Construct the microclusters using a CF-Tree

Hello Sema, On 30/06/17 17:14, Sema Atasever wrote: I want to cluster them using Birch clustering algorithm. Does this method have 'precomputed' option. No it doesn't, see http://scikit-learn.org/stable/modules/generated/sklearn.cluster.Birch.html so you would need to provide it with the ori

Re: [scikit-learn] Agglomerative clustering

You can have a look at the test named "test_agglomerative_clustering" in: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cluster/tests/test_hierarchical.py -- Olivier ___ scikit-learn mailing list scikit-learn@python.org https://mail.

Re: [scikit-learn] Fwd: [SciPy-User] EuroSciPy 2017 call for contributions - extension of deadline

Dear Communities, Would it be of interest to the audience to hear a discussion on the state of the art in computational drug discovery model development, which my team and I have done by building on top of Scikit-learn and Matplotlib? Everyday language description of the work and concept: https://

[scikit-learn] Scikit-learn at Data Intelligence this past weekend

This past weekend was the Numfocus sponsored Data Intelligence conference at Capital One, in Mclean, Virginia (close to Washington DC for those not familiar with the US geography). A few presentations mentioned/used scikit-learn, including Ben Bengfort's Visual Pipelines ( http://data-intelligence

[scikit-learn] Agglomerative Clustering without knowing number of clusters

I want to perform agglomerative clustering, but I have no idea of number of clusters before hand. But I want that every cluster has at least 40 data points in it. How can I apply this to sklearn.agglomerative clustering? Should I use dendrogram and cut it somehow? I have no idea how to relate dendr

Re: [scikit-learn] Scikit-learn at Data Intelligence this past weekend

Thanks for this report! -- Olivier ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

Re: [scikit-learn] Fwd: [SciPy-User] EuroSciPy 2017 call for contributions - extension of deadline

I am pretty sure this is exactly the kind of presentation that the EuroScipy audience would enjoy. Please submit! -- Olivier ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

Re: [scikit-learn] Scikit-learn at Data Intelligence this past weekend

Thanks for the summary. I was there as well, and it seemed like scikit-learn had a strong showing. It seemed as though many talks that weren't directly on scikit-learn still mentioned it or used the models during the presentation. On Fri, Jun 30, 2017 at 9:47 AM, Francois Dion wrote: > This past

Re: [scikit-learn] Scikit-learn at Data Intelligence this past weekend

Fantastic! Thanks a lot for the summary. Gaël On Fri, Jun 30, 2017 at 12:47:35PM -0400, Francois Dion wrote: > This past weekend was the Numfocus sponsored Data Intelligence conference at > Capital One, in Mclean, Virginia (close to Washington DC for those not > familiar > with the US geography)