[scikit-learn] Which algorithm is used in sklearn SGDClassifier when modified huber loss is used?

[Vikas Kumar]

The documentation says:

The loss function to be used. Defaults to ‘hinge’, which gives a linear
SVM. The ‘log’ loss gives logistic regression, a probabilistic classifier.
‘modified_huber’ is another smooth loss that brings tolerance to outliers
as well as probability estimates.

When we use 'modified_huber' loss function, which classification algorithm
is used? Is it SVM? If yes, how come it is able to give probability
estimates, which is something it can't do with hinge loss?

Regards,

Vikas
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