Awesome!
On 10/23/18 9:10 AM, Mathieu Blondel wrote:
Hi,
Most scikit-learn users who need predict_proba use the logistic
regression class. We've released a new package implementing more loss
functions useful for probabilistic classification.
https://github.com/mblondel/fenchel-young-losses/
This is based on our recently proposed family of loss functions called
"Fenchel-Young losses" [*].
Two distinguishing features that should be of interest:
1) You can call fit(X, Y) where Y is a n_samples array of label
integers *or* Y is a n_samples x n_classes array containing *label
proportions*.
We've gotten that feature request for logistic regression a couple of
times, not sure it's in the scope of scikit-learn.
Great to see that you've done it!
2) predict_proba(X) is able to output *sparse* probabilities for some
choices of loss functions (loss="sparsemax" or loss="tsallis"). This
means that some classes may get *exactly* zero probability.
Both features are especially useful in a multi-label setting.
We've also released drop-in replacements for PyTorch and Tensorflow
loss functions in the same package.
Feedback welcome!
Cheers,
Mathieu
[*] https://arxiv.org/abs/1805.09717
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