When we fit a generalized linear regression (e.g., logistic regression, gamma regression) we are estimating the population average Y given the predictors $X$ ( i.e., $E(Y | X)$ ).
When we fit a machine learning model such as an ANN, SVM, or a decision tree, does this notion still apply? In other words, are we estimating the population average value of $Y$ or isn't that idea applicable and we are just predicting "Y"?
ADD After Dikran response:
I. What aspect of the theory of a predictive modeling algorithm tells us that we are modeling E(Y|X) versus just Y|X ? Is it the use of an error term that follows a certain distribution? For instance, what is it about ANN versus a decision tree which tells us the former models E(Y|X) while the latter is modeling Y|X?
II. Is there any connection between these and say a confidence interval versus prediction interval in linear regression?