Sure, go for it! :) 

Sent from my iPhone

> On Nov 12, 2015, at 1:41 PM, olologin <[email protected]> wrote:
> 
>> On 11/12/2015 08:29 PM, Sebastian       Raschka wrote:
>> I agree, this sounds like a very useful addition. Why I was a bit hesitant 
>> about adding it to the initial implementation was that I was/am not sure 
>> what happens if, for example, in cases such as
>> 
>> cross_val_score w. n_jobs > -1
>>        VotingClassifier w. n_jobs > -1
>>                  RandomForest w. n_jobs > -1
>> 
>> Using the code from cross_val_score, would this safely execute? I think if 
>> the outer functions would use up all of the CPUs already, the inner 
>> functions would basically run on a single CPU without throwing errors!? I 
>> could try adding multiprocessing to the VotingClassifier, I think that’s 
>> very useful in certain situations where the estimators inside don’t use 
>> multiprocessing themselves. Or I could leave this feature for you to 
>> implement if you like ;) ??
>> 
>>> On Nov 11, 2015, at 8:27 PM, Scott Turner <[email protected]> wrote:
>>> 
>>> On Wed, Nov 11, 2015 at 6:18 PM, 
>>> <[email protected]> wrote:
>>>> @Scott: See https://github.com/scikit-learn/scikit-learn/pull/5794 for 
>>>> checking the VotingClassifier
>>> 
>>> It has also occurred to me that VotingClassifier ought to be capable of 
>>> using multiple CPUs to run the base classifiers, ala cross_val_score.  The 
>>> code there is fairly easy to copy.
>>> 
>>> -- Scott
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>> 
>> 
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> Sorry for interruption, i can handle this if no one wants. I've already 
> started implementation of it 20 minutes ago :)
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