Interesting - covtype involves a number of categorical attributes
which are represented via a one-hot encoding - do you think that such
a representation has a significant effect on feature sampling and thus
the performance of random forests?

2012/3/27 Gilles Louppe <[email protected]>:
> Hi,
>
> I am running the tests again, but indeed I think the difference in the
> results comes from that fact that max_features=sqrt(n_features) now by
> default whereas it was max_features=n_features before.
>
> Gilles
>
> On 27 March 2012 11:56, Paolo Losi <[email protected]> wrote:
>> Thanks Peter,
>>
>> On Tue, Mar 27, 2012 at 11:34 AM, Peter Prettenhofer
>> <[email protected]> wrote:
>>>
>>> Paolo,
>>>
>>> I noticed that too - maybe @glouppe can comment on this - I think the
>>> reason was a change in the ``n_features`` heuristic but I might be
>>> mistaken.
>>
>>
>> Gilles, can you give a quick look to it? If it's not anything obvious just
>> ping back and I'll try to git bisect the issue...
>>
>>>
>>> Concerning the GaussianNB - there's a PR [1] adressing a critical bug
>>> in the estimator - it should be merged ASAP.
>>
>>
>> Thank's. I've commented on the PR (the performance regression seems
>> not to be connected with the PR)
>>
>>>
>>> Furthermore, test time is
>>> quite low - this might be due to memory layout issues - SGDClassifier
>>> converts ``coef_`` to fortran-style for increased test-time
>>> performance.
>>
>>
>> Clear.
>>
>> Thanks again
>>
>> Paolo
>>
>>
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-- 
Peter Prettenhofer

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