The Backward elimination method
To select features automatically we iteratively fitted random forests, at each iteration building a new forest after discarding 20% of the features with the smallest variable importance. The selected set of features was used as a predictor to fit the model to check the ‘out of bag’ (OOB) error rate. We examined the OOB error rates from all fitted random forests. We chose the solution with the smallest number of variables whose error rate was within one standard error. This procedure was performed iteratively using the varSelRF package in R (v3.6.1).