When using logistic regression in Python's scikit-learn, one may handle multiclass problems even with binary logistic regression. If there are $K$ classes, then coefficients (i.e. weights and biases) for $K$ logistic functions will be produced. But this is using a 'one vs. rest' approach, and the probabilities from the individual logistic functions won't necessarily add up to 1 since this is binary logistic regression. Therefore, when using
predict_proba with sklearn's logistic regression, how are probabilities handled in multiclass problems?
I've investigated this and it appears to be similar to applying a softmax function to the individual probabilities of the $K$ logistic functions, but this is not exactly correct. I also do not see explicit mention of this in the documentation.