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.

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