Feature importance based on extremely randomize trees and feature redundancy
Maybe you could extract the top n important features and then compute pairwise Spearman's or Pearson's correlations for those in order to detect redundancy only for the top informative features as it might not be feasible to compute all pairwise feature correlations (quadratic with the number of features).
There might be more clever ways to do the same by exploiting the statistics of the relative occurrences of the features as nodes in the decision trees though.
I am using the Scikit-learn Extremely Randomized Trees algorithm to get info about the relative feature importances and I have a question about how "redundant features" are ranked.
If I have two features that are identical (redundant) and important to the classification, the extremely randomized trees cannot detect the redundancy of the features. That is, both features get a high ranking.  Is there any other way to detect that two features are actualy redundant?