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*. 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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