Comparing VW and liblinear seems pretty meaningless (and calling 
liblinear Python is also odd).

It's clear that there are faster gbm packages (and it seems random 
forests for some settings of
the parameters)
We recently had some improvements to the trees and it would be 
interesting to benchmark again.
Still, this is only a single dataset, and not really a serious attempt 
at a benchmark
(which is really hard).
It would be awesome to have real benchmarks on something like openml.org



On 11/05/2015 08:38 AM, Gael Varoquaux wrote:
> On Thu, Nov 05, 2015 at 07:05:11AM +0000, Raphael C wrote:
>> https://github.com/szilard/benchm-ml
>> The upshot is that in some cases it seems that the scikit-learn
>> versions have room for improvement.
> The various main lessons that I can see from those results are:
>
> * Linear models (aka LogisticRegression) don't scale very well:
>
>    - The page benches the default, which is liblinear.
>      I would be very curious to see how the other solvers (Newton, and
>      SAG) fair on this dataset.
>      It would be useful to introduce a 'solver="auto"' for logistic
>      regression, based on heavy benchmarks and heuristics.
>      I have created an issue about this, to discuss if we want to do this:
>      https://github.com/scikit-learn/scikit-learn/issues/5736
>
>    - Having fused types to avoid increased memory would be useful.
>      For this we first need to finish adding cython as a build dependency:
>      https://github.com/scikit-learn/scikit-learn/pull/5492
>    
> - In tree-based Not handling categorical variables as such hurts us a lot
>    There's a PR to fix that, it still needs a bit of love:
>    https://github.com/scikit-learn/scikit-learn/pull/4899
>
> Gaƫl
>
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