There are several packages (spearmint, hyperopt, MOE) offering Bayesian
Optimization to the problem of choosing hyperparameters. Wouldn't it be
nice to add such *Search[CV] to sklearn?
On Thu, Feb 12, 2015 at 12:33 PM, Mathieu Blondel <math...@mblondel.org>
wrote:
> A grid-search related project could be useful:
>
> - multiple metric support (e.g., find the best model w.r.t. f1 score and
> the best model w.r.t. AUC)
> - data independent cv iterators (
> https://github.com/scikit-learn/scikit-learn/issues/2904)
> - anything else?
>
> Mathieu
>
> On Thu, Feb 12, 2015 at 5:53 PM, Gael Varoquaux <
> gael.varoqu...@normalesup.org> wrote:
>
>> > How about adding partial_fit to existing low rank methods or new
>> incremental
>> > algorithms?
>>
>> I think that making scikit-learn scale better is an important alley for
>> the future. Thus I would personnally see very well any kind of efforts in
>> this direction. However, these need to be well technically motivated:
>> feasability in terms of code, but also robustness to hyper parameters.
>>
>> Gaël
>>
>>
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>
>
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