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The hinge_loss metrics could be improved by adding support multiclass hinge loss. For more information, see wikipedia and the narrative documentation.
This would require to enhance the current code, write tests and modify the documentation accordingly.
There are several multiclass variants of the hinge loss. The one in the article you link is the variant by Crammer & Singer. We should either accept a parameter to let the user choose or use one function per variant.
We can also implement the "one-vs-rest" loss, which is simply to sum the hinge losses of each binary classification problem.
Thanks @mblondel for the precision.
I am new to Scikit-learn. I am trying to fix this issue.
Hi, I am reading this paper
in order to understand the notation of multi class hinge loss equation at wikipedia(w subscript y and w subscript t). Can anyone please confirm if w subscript f and l are functional margins? Are they product of w evaulated at some feature and the corresponding product?
Also, is there any way to find w from est?
Fixed by #3607