I imagine a lot of people might be interested in this, but be in a position
where they need to justify bringing in a new package that mimics sklearn,
rather than just using the linear models that are already available there.
Could you day a but more about how/why this is better?
Thanks!
Fred.
On Mar 25, 2016 9:10 AM, "Fabian Pedregosa" <f...@bianp.net> wrote:
> Dear all,
>
> I'm pleased to announce the first public release of lightning (
> http://contrib.scikit-learn.org/lightning/).
>
> lightning is a library for large-scale linear classification, regression
> and ranking in Python.
>
> Highlights:
>
> * follows the scikit-learn API conventions.
> * supports natively both dense and sparse data representations.
> * computationally demanding parts implemented in Cython.
>
> The release can be downloaded from pypi:
>
> https://pypi.python.org/pypi/sklearn-contrib-lightning
>
> and prebuild conda packages can be installed with the command
>
> conda install -c https://conda.anaconda.org/scikit-learn-contrib
> lightning
>
> Check out the website for more information:
>
> http://contrib.scikit-learn.org/lightning/
>
>
> Happy hacking!
>
> the lightning team
>
>
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