Yeah actually they can only be better if the data is memmaped in
advanced (for instance using joblib.dump(data, filename) /
joblib.load(filename, mmap_mode='c')). Also this is only really
interesting for large datasets (e.g. larger than 100MB) which is
probably not the case here in retrospect.

2012/11/17 Peter Prettenhofer <[email protected]>:
> Olivier,
>
> I tested it with the joblib PR - results got a bit worse.
>
> see below
>
> best,
>  Peter
>
> ________________________________________________________________________________
> arcene
>
>               r               py
> score   0.2700 (0.03)   0.2633 (0.02)
> train   3.9454 (0.09)   4.6661 (0.20)
> test    0.2199 (0.00)   0.2985 (0.05)
>
> ________________________________________________________________________________
> landsat
>
>               r               py
> score   0.0255 (0.00)   0.0552 (0.00)
> train   2.3184 (0.02)   3.8349 (0.06)
> test    0.1129 (0.00)   0.3513 (0.01)
>
> ________________________________________________________________________________
> spam
>
>               r               py
> score   0.0549 (0.00)   0.0664 (0.00)
> train   1.6380 (0.01)   2.1307 (0.02)
> test    0.0379 (0.00)   0.3311 (0.00)
>
> ________________________________________________________________________________
> random_gaussian
>
>               r               py
> score   0.1449 (0.00)   0.1487 (0.01)
> train   0.3371 (0.01)   1.3574 (0.04)
> test    0.1502 (0.00)   0.3247 (0.05)
>
> ________________________________________________________________________________
> madelon
>
>               r               py
> score   0.4061 (0.01)   0.3867 (0.02)
> train   10.0216 (0.08)  10.4346 (0.08)
> test    0.0980 (0.00)   0.3221 (0.02)
>
>
>
> 2012/11/17 Olivier Grisel <[email protected]>:
>> You can retry by replacing the sklearn/externals/joblib folder with
>> the joblib folder of this branch:
>>
>> https://github.com/joblib/joblib/pull/44
>>
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>
>
>
> --
> Peter Prettenhofer
>
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-- 
Olivier
http://twitter.com/ogrisel - http://github.com/ogrisel

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