Random forest

Random Forest Variable Importance

Random forests ™ are great. They are one of the best "black-box" supervised learning methods. If you have lots of data and lots of predictor variables, you can do worse than random forests. They can deal with messy, real data. If there are lots of extraneous predictors, it has no problem. It automatically does a good job of finding interactions as well. There are no assumptions that the response has a linear (or even smooth) relationship with the predictors.