There have been previous attempts to incorporate training score, but
there's a general open question of how best to return Gird Search results:
The current format cv_scores_ is not really extensible, which seems to have
stalled many of these issues. Input on this issue is welcome. Otherwise,
for the moment, you will have to roll your own implementation (and I should
note that _fit_and_score is a fairly recent invention).
On 2 May 2014 13:34, Robert McGibbon <rmcgi...@gmail.com> wrote:
> Hi all,
>
> Is there any to get the score on the training data for each parameter set
> (and each fold) when running GridSearchCV? While I haven't looked too
> closely at the code, it appears that
> BaseSearchCV<https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/grid_search.py#L378>uses
> the
> _fit_and_score<https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/cross_validation.py#L1118>
> method,
> which does have the ability to calculated and return scores on the training
> data, but that this functionality isn't exposed in GridSearchCV.
>
> The use case for this would to compare training and test error (ala the
> classic training error and test error vs. model complexity
> plot<http://link.springer.com/protocol/10.1007%2F978-1-60327-429-6_15/fulltext.html#Fig3_15>
> )
>
> -Robert
>
>
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