Regression with discrete targets The model I train is used to predict a quasi-continuous target variable (e.g. a time). The predicted time can be used to calculate a resulting score, but this is computationally expensive. I precomputed scores for discrete times that I can look up, however, the output from the regression is continuous. Can I force the regression to 'choose' a discrete time or do I have to use a classifier?
The disadvantage of the classifier would be that I does not understand that e.g. class 2s is smaller than class 3s and bigger than class 1s.
The framework is sklearn for now, using a decision tree regressor.

I guess it may have too much labels if using classifier, and since it is quasi-continuous, so regression may get better performance since it use more information than classification. Maybe you can try get the prediction from the regression and transform them based on distance to nearby targets to be discrete, like 1.02s -> 1s since 1.02 is more close to 1s than 2s.
