A random forest is a form of ensemble method for machine learning. Large numbers of decision trees are generated by choosing random subsets of features from random subsets of the training data. The indivdiual trees each give their own results and a separate layer of processing combines the results from all of the trees to give a final result. While compartively simple, random forests have proved highly effective in a wide variey of application domains.
Used in Chap. 5: page 77; Chap. 8: page 119; Chap. 9: page 131; Chap. 16: pages 266, 267; Chap. 18: page 303
Also known as random decision forest
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Random Forest