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Novel variable interaction measures with random forest classifiers are proposed. The proposed methods efficiently measure the change in classification performance due to non-linear interactions between variables by exploiting random permutation of out-of-bag samples in random forests. They can be readily extended to measure n-subset interactions in multi-class bagging ensembles with any base supervised...