[scikit-learn] Sparse predict_proba and Fenchel-Young losses
mathieu at mblondel.org
Tue Oct 23 09:10:49 EDT 2018
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.
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.
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