I understand that random forest regression has a numeric response variable, and random forest classification has a categorical response variable. The response for my data is always an integer representing a rate per hour. It is fairly constant over time. I have one year of hourly data and only 31 different possible rates appear in that time. In my application, the rate cannot be a decimal, and it also cannot be 0 or negative.
I fit a RF regression and a RF classification to compare the results. (using randomForest in R)
My regression does not output integers, so I round the result to the nearest whole number.
The classification model is concerning, because there is a range of possible values, say [0,N], but not every possible value occurs in the historic data, so these classes will never be predicted.
Assuming random forest is the appropriate model here (and I understand it may not be), I want to understand if I should use a classification or regression on this data, and how to arrive at that decision. Since my data is numeric, is it inappropriate to use a classification?