Peter - Yes. That also puzzles me. So odd.
Thanks Olivier - I am using auc_score, not roc_curve. My scikit-learn
installation does not complain about it. I will try to get the master git
installed.
Josh
On Mon, Jul 8, 2013 at 4:48 PM, Peter Prettenhofer <
[email protected]> wrote:
> What is actually quite interesting is that the "worst" model has AUC of
> 0.29 which is actually AUC 0.71 if you invert the predictions.
>
>
> 2013/7/8 Olivier Grisel <[email protected]>
>
>> Alternatively you can use the `score_func=f1_score` in 0.13 look for
>> models that trade off precision and recall on unbalanced datasets.
>>
>> --
>> Olivier
>>
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>
> --
> Peter Prettenhofer
>
>
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