Computational prediction of diagnosis and feature selection on mesothelioma patient health records
Fig 5
Gini impurity decreases of each random forest tree node.
Random forest feature selection rely on bootstrap aggregation (bagging), and therefore does not have training set, validation set, and test set [69]. The bars represent the importance of each feature, measured through the sum of all the Gini impurity index decreases for each specific feature [39] (Methods).