where is the classifier in the proof of theorem if $tilde(x) is the regression function, how does it work how does the proof of theorem differ between linear regression and classifier based on the classifier what is the classifier of $g in the proof of theorem 2.2 what is the proof of theorem 2.2 what type of regression is $g(x)? when to use regression as a classifier which of the following is the proof of theorem 2.2 what is the difference between the regression function and the classifier what is the approximation of bayes's method g(x) how to find inequalities in regression what is the proof of theorem for regression what the proof of theorem for inequality how does $geq(x) differ from $tilde(x) what is the meaning of g(x)? how to find out if a regression function is inequal how do you find the regression function? which type of classifier represents inequalities who is the proof of k? what is the classifier for the bayes classifier and a regression function