One of the desired features of a classifier is that it generates probabilities for us. So we know with what probability a sample belongs to class A or class B (in the case of a binary classification problem). However it has been shown that the probabilities that the classifiers produce are not always correct.

A correct value means that e.g. a classification for sample s with probability 0.8 would eventually mean that among samples that have been assigned a probability of 0.8, 80% actually belong to class A and 20% of them belong to class B. This is a hand problem to prove.

Known classification algorithms have been known to have different mistakes when assigning these probabilities.

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So there have been some approaches on calibrating these probabilitiws. Such that once these classifiers generate a probability it actually means that probability. It's not just a number between zero and one.

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