# Probability in logistic regression VS machine learning

I have read somewhere that a bonus of using logistic regression (as a classic statistical tool) is that is gives out probabilities. But then by reviewing all the other machine learning methods, most of them give out some continuous scores that can be converted into probability instead of thresholding.

Is there a difference between these two? Are the probabilities from LR somehow superior to those of other ML methods?