Are there any methods that one could utilize to make Random Forest more interpretable? Random Forest performs much better than CART but it is a lot less interpretable.
The results from CART can change easily (with realistic sample sizes) with small perturbations to the data. If this is the case, it seems the interpretation is not a straightforward as it seems. I've often heard some of my colleagues avoiding random forests because of difficulties in interpretation. They are built more for prediction. Even the variable importance measures that come out are based on predictive performance, but they do help with interpretation.
For each tree in the forest you have an interpretation for the terminal nodes. So the forest can be viewed as a series of explanations why vector x might belong to class y. Then the class with the largest number of reasonable explanations is the class that is picked. Isn't that fairly easy to understand?