Classification vs. Regression

Two slightly similar concepts in supervised machine learning are Supervised classification, and regression.
With supervised classification you will get a discrete output (a label or boolean value) and in regression your output is continuous (i.e. a number).
The thing you are trying to find in the different cases is a decision boundary when using classification and a best fit line in regression. You evalueate the former with it’s accuracy value, and the latter using the “sum of squared errors” or r².