I'm new to analytics, currently I'm working on a dataset which has more than 100 variables and I having checking out the different possible methods to reduce it to input it to the regression model. While searching I have come decision trees to be one of the best methods for variable reduction. My predictor variable or independent variables are categorical and my dependent variable is a continuous variable. I would like to know whether decision trees work on this scenario, if so I would want to know the logic behind it or if not is there any other variable reduction technique for best possible prediction. In decision trees how do we decide the cutoff and variables..?
Thanks in advance.