问题
I'm working on a multiclass classification problem with different classifiers, working with Python and scikit-learn. I want to use the predicted probabilities, basically to compare the predicted probabilities of the different classifiers for a specific case.
I started reading about 'calibration' (here and here for example) and I became confused.
For what I understood: a well-calibrated probability means that that a probability also reflects the fraction of a certain class.
1) Does this imply that if I have 10 equally distributed classes, the calibrated probabilities would ideally be around 0.1 for every class?
2) Can I interpret the probabilities of predict_proba (without calibration) as 'how certain is the classifier about this being the correct class'?
Hopefully, someone can clarify this for me! :)
来源：https://stackoverflow.com/questions/60110209/multiclass-classification-probabilities-and-calibration