If anyone is interested in implementing these, dask-ml would welcome additional metrics that work well with Dask arrays: https://github.com/dask/dask-ml/issues/213.
On Tue, May 14, 2019 at 2:09 AM Uri Goren <ugo...@gmail.com> wrote: > Sounds like you need to use spark, > this project looks promising: > https://github.com/xiaocai00/SparkPinkMST > > On Tue, May 14, 2019 at 5:12 AM lampahome <pahome.c...@mirlab.org> wrote: > >> >> Uri Goren <ugo...@gmail.com> 於 2019年5月3日 週五 下午7:29寫道： >> >>> I usually use clustering to save costs on labelling. >>> I like to apply hierarchical clustering, and then label a small sample >>> and fine-tune the clustering algorithm. >>> >>> That way, you can evaluate the effectiveness in terms of cluster purity >>> (how many clusters contain mixed labels) >>> >>> See example with sklearn here : >>> https://youtu.be/GM8L324MuHc?list=PLqkckaeDLF4IDdKltyBwx8jLaz5nwDPQU >>> >>> >>> But if my dataset is too large to load into memory, will it work? >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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