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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?

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  • $\begingroup$ The docs state that the modified_huber loss leads to calibrated responses that are converted to probabilities using an affine transformation (normalization). $\endgroup$
    – Emre
    Commented Jul 6, 2017 at 18:08

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The modified Huber loss is equivalent to a quadratically smoothed SVM with gamma = 2.

See also https://www.quora.com/What-algorithm-is-used-in-sklearn%E2%80%99s-SGDClassifier-when-a-modified-huber-loss-is-used/

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