On Tuesday, July 22, 2014 4:21:49 AM UTC-5, Viral Shah wrote: > > Wow, better than scikit.learn? This is exciting. >
We are not there yet. However, the work to unify our many packages for regression has already been started. If we keep our paces this won't be a too-far-away goal. > > We should probably discuss in the roadmap issue about what infrastructure > we need to support large-scale distributed machine learning problems. > > -viral > > On Monday, July 21, 2014 4:08:14 AM UTC+5:30, Dahua Lin wrote: >> >> Please see https://github.com/JuliaStats/MLBase.jl/blob/master/NEWS.md >> for recent updates. >> >> Also the documentation is moved from Readme to a Sphinx doc >> <http://mlbasejl.readthedocs.org/en/latest/> >> >> Now we already have quite a few packages for various machine learning >> tasks: >> >> MLBase.jl <https://github.com/JuliaStats/MLBase.jl>: data preprocessing, >> performance evaluation, cross validation, model tuning, etc >> Distance.jl <https://github.com/JuliaStats/Distance.jl>: metric/distance >> computation (including batch & pairwise computation) >> MultivariateStats.jl <https://github.com/JuliaStats/MultivariateStats.jl>: >> multivariate analysis, ridge regression, dimensionality reduction >> Clustering.jl <https://github.com/JuliaStats/Clustering.jl>: K-means, >> K-medoids, Affinity propagation >> NMF.jl <https://github.com/JuliaStats/NMF.jl>: Nonnegative matrix >> factorization >> >> In addition, we have a bunch of other packages for Regression, GLM, SVM, >> etc. We are now beginning to unite the efforts in this domain (see the >> discussion <https://github.com/JuliaStats/Roadmap.jl/issues/14> here). >> >> We have been making steady progress, and I believe that we will have a >> great machine learning ecosystem, one that is comparable or even superior >> to scikit.learn in not too long future. >> >> Cheers, >> Dahua >> >>
