You can indeed tune parameters of the RF with grid search, and the score
method will be used although you could specify a different task metric to
GridSearchCV's scoring parameter.
On 24 September 2014 07:50, Pagliari, Roberto <rpagli...@appcomsci.com>
wrote:
> I’m a bit confused as to why gridsearchCV is not needed with random
> forests. I understand that with RF, each tree will only get to see a
> partial representation of the data.
>
>
>
> However, if I wanted to tune some parameters of the RF, wouldn’t I still
> need to do gridsearch? If that is the case, does sklearn use the out of bag
> error to find the best classifier, or the score method?
>
>
>
> Thank you,
>
>
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