With lightning, you can train linear models on large-scale data using
recent state-of-the-art optimization algorithms which are too cutting-edge
for including in scikit-learn (e.g., SDCA or SAGA). If you just want to
train a logistic regression on 1000 samples, you don't need lightning :)
Mathieu
On Fri, Mar 25, 2016 at 11:13 PM, Fred Mailhot <fred.mail...@gmail.com>
wrote:
> 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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