<p>Although a softmax activation will ensure that the outputs satisfy the surface Kolmogorov axioms (probabilities always sum to one, no probability below zero and above one) and the individual values can be seen as a measure of the network's confidence, you would need to calibrate the model (train it not as a classifier but rather as a probability predictor) or use a bayesian network before you could formally claim that the output values are your per-class prediction confidences. (<a href="https://arxiv.org/pdf/1706.04599.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1706.04599.pdf</a>)</p>
Can a probability score from a machine learning model XGBClassifier or Neural Network be treated as a confidence score?
<p>If there are 4 classes and output probability from the model is A=0.30,B=0.40,C=0.20 D=0.10 then can I say that output from the model is class B with 40% confidence? If not then why?</p>