[scikit-learn] Introducing creme for online learning

Hello everyone, I sometimes see emails where people are asking about training models incrementally. Me and some friends have started a Python library for doing so-called online learning named creme: https://github.com/creme-ml/creme. The code is idiomatic and the API resembles that of sklearn. Onl

Re: [scikit-learn] ANN: scikit-learn 0.21 released

Indeed! Great improvements. And it's a pleasure to see that the releases are more frequent: a huge value to the community. Gaël On Thu, May 16, 2019 at 10:21:09AM +0200, bertrand.thirion wrote: > Congratulations ! > Bertrand > Envoyé depuis mon smartphone Samsung Galaxy. > Message

Re: [scikit-learn] ANN: scikit-learn 0.21 released

Congratulations !Bertrand Envoyé depuis mon smartphone Samsung Galaxy. Message d'origine De : Joel Nothman Date : 16/05/2019 10:03 (GMT+01:00) À : Scikit-learn user and developer mailing list Objet : [scikit-learn] ANN: scikit-learn 0.21 released Thanks to the work of many,

[scikit-learn] ANN: scikit-learn 0.21 released

Thanks to the work of many, many contributors, we have released Scikit-learn 0.21. It is available from GitHub, PyPI and Conda-forge, but is not yet available on the Anaconda defaults channel. * Documentation at https://scikit-learn.org/0.21 * Release Notes at https://scikit-learn.org/0.21/whats_n

Re: [scikit-learn] Can I evaluate clustering efficiency incrementally?

The contingency matrix ( https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.contingency_matrix.html) counts how many times each pair of (true cluster, predicted cluster) occurs. It is sufficient statistics for every "supervised" (i.e. ground truth-based) clustering evaluation