The Boruta algorithm is based on other algorithms that can provide importance scores of features. In addition to real features, the same number of "fake" features is added. Fake features are made from real ones by random shuffle of rows. A feature is considered "important" if its importance score is better than the best "fake" feature. The final importance is the number of repeats in which this feature was considered as important. Feature importance scores provided by random forest are based on the decrease of the impurity measure when the corresponding variable is used. For the Boruta algorithm , we use our own implementation of the algorithm. We use implementations of random forest from the Smile package (version 2.6.0) with default hyperparameters for initial importance score of features. 80 rounds of the Boruta algorithm were used.