Have access to a dataset with hundreds of variables and millions of cases (American Community Survey).
Need to identify a small, manageable set of Independent Variables (IVs) to use for Multiple Regression.
One way to do this, of course, would be to use applicable theories to identify the IVs.
Was wondering how I could use a data-driven (data-mining?) approach as follows:
- Use a Decision Tree to identify impactful (candidate? relevant?) IVs?
- And then use these as the IVs in the Multiple Regression?
(Seem to remember reading once, in passing, that this approach to variable reduction is permitted.)
Tried searching on Google for articles that clarify the above, but the search terms are such that I keep getting hits to articles that compare Decision Trees and Multiple Regression.
So, if you know of articles and research papers that describe how to do the above, please leave links below. Also, I would welcome your own original suggestions on how to proceed.