>> Does scikit have a function to find the maximum f1 score (and decision >> threshold) for a (soft) classifier?
Hm, I don't think so. F1-score is typically used as evaluation metric; hence, it's something optimized via hyperparameter tuning. There's an interesting publication though, where the authors modified the F1 score so that it's differentiable and can be used as a cost function for optimization/training: Maximum F1-Score Discriminative Training Criterion for Automatic Mispronunciation Detection: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7055841 Best, Sebastian > On Jul 17, 2017, at 4:12 PM, Stuart Reynolds <stu...@stuartreynolds.net> > wrote: > > 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 > <stu...@stuartreynolds.net> wrote: >> 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 mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
