I'm training a random forest regression model on a dataset that consinsts of values in the range of 0-50. It has many values close to zero and only 500 observations. The R^2 is also small, about 0.25-0.4. When I use the model on a new test data set I get predictions exceeding the range of 0-50, how can I deal with that?
Sometimes I also get predicted values only in the range of 10-40 but I'm certain there should be many zeros and at least one 50.
Is there a better algorithm that can handle this problem?
I'm using R and the
caret package. Thanks.