Research Article
Unbiased Feature Selection in Learning Random Forests for High-Dimensional Data
input: : the training data set, | : the number of trees, | mtry: the size of the subspaces. | output: A random forest RF | (1) for to do | (2) Draw a bagged subset of samples from . |
(4) while (stopping criteria is not met) do | (5) Select randomly mtry features. | (6) for to do | (7) Compute the decrease in the node impurity. | (8) Choose the feature which decreases the impurity the most and | the node is divided into two children nodes. | (9) Combine the trees to form a random forest. |
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