Hi Andy,
I'm following up on this from a while ago, because I finally got around to
trying to implement it.
To get the minority f1 score from my grid search, I have:
min_f_scorer = make_scorer(f1_score(pos_label=1))
grid_search = GridSearchCV(pipeline, parameters, verbose=1, cv=3,
scoring=min_f1_scorer)
But the f1_score() constructor needs the y_true and y_pred arrays, right?
I'm not sure what I'm doing wrong, since it would only have y_pred after
the classifier was run.
Thanks again,
Adam
On Fri, May 8, 2015 at 12:39 PM, Andreas Mueller <t3k...@gmail.com> wrote:
> Hi Adam.
> You can get the f_score by setting ``GridSearchCV(...,
> scoring='f1_score')``.
> By default, the "positive" class if the one labeled "1":
>
> http://scikit-learn.org/dev/modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score
>
> If you want more control, you can do
> my_f_scoring = make_scorer(f1_score, pos_label="minority_label",
> other_arguments=that_you_want)
> GridSearchCV(..., scoring=my_f_scoring)
>
> You can not compute accuracy and f1 score at the same time, though, which
> is a known limitation, which we will fix soon.
>
> Cheers,
> Andy
>
>
>
> On 05/08/2015 11:57 AM, Adam Goodkind wrote:
>
> In performing a grid search, I know we can get overall accuracy, but I'm
> also interested in the minority F score. Is there a way to get that for
> each parameter setting combination?
>
> Thanks,
> Adam
>
> --
> *Adam Goodkind *
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> @adamgreatkind <https://twitter.com/#%21/adamgreatkind>
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@adamgreatkind <https://twitter.com/#!/adamgreatkind>
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