I want to evaluate the importance of each of the features of a 2000x60 dataset in a classification problem with random forest.
The most widely used ones apparrently are:
- Cross Entropy-Information Gain
- Gini Importance (
- Mean Squared Error (
I have also found a rather concise overview of some other metrics for variables' importance at random forests at this research paper.
These are the following:
- Recurrent relative variable importance
- Recursive feature elimination
Has anyone used these and which one was the most informative for his/her model?
Do you have any other metrics of this kind for variable importance at random forests?