Re: [scikit-learn] Max f1 score for soft classifier?

That was also my thinking. Similarly it's also useful to try and choose a threshold that achieves some tpr or fpr, so that methods can be approximately compared to published results. It's not obvious what to do though when an increment in the threshold results in several changes in

Re: [scikit-learn] Max f1 score for soft classifier?

I suppose it would not be hard to build a wrapper that does this, if all we are doing is choosing a threshold. Although a global maximum is not guaranteed without some kind of interpolation over the precision-recall curve. On 18 July 2017 at 02:41, Stuart Reynolds

Re: [scikit-learn] scikit-learn 0.19b2 is available for testing

Great work indeed ! Thx, Bertrand On 17/07/2017 22:08, Alexandre Gramfort wrote: great team work as usual ! congrats everyone Alex ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

Re: [scikit-learn] Max f1 score for soft classifier?

And... with that in mind -- are there methods that explicitly try to optimize the f1 score? On Mon, Jul 17, 2017 at 9:41 AM, Stuart Reynolds wrote: > Does scikit have a function to find the maximum f1 score (and decision > threshold) for a (soft) classifier? > > -

Re: [scikit-learn] scikit-learn 0.19b2 is available for testing

Great job! This will be a great release, with a lot of new features and improvements G On Mon, Jul 17, 2017 at 02:49:51PM +0200, Olivier Grisel wrote: > The new release is coming and we are seeking feedback from beta testers! > pip install scikit-learn==0.19b2 > conda-forge packages should

[scikit-learn] Max f1 score for soft classifier?

Does scikit have a function to find the maximum f1 score (and decision threshold) for a (soft) classifier? - Stuart ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

[scikit-learn] scikit-learn 0.19b2 is available for testing

The new release is coming and we are seeking feedback from beta testers! pip install scikit-learn==0.19b2 conda-forge packages should follow in the coming hours / days. Note that many models have changed behaviors and some things have been deprecated, see the full changelog at: