I have two Questions regarding when to choose Classification and Regression methods.
In my experiment, 'target' can be coded either by one of the two features, one feature is numerical discrete and other numerical continuous. In my set of experiments the numerical continuous feature is rounded off to nearest round figure but its time so its continuous. It can be coded by both individually, its upto me which feature to use to code my target (I will code by one of the features). Then will it be regression case or classification in supervised machine learning?
But what if i chose other feature to code my target, the discrete feature, then can I apply regression to it ?
As from what I know so far, Regression methods are applied when response being predicted is ordered and continuous. (I dont know more detail about it)