Erwan Scornet

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The random forest algorithm, proposed by L. Breiman in 2001, has been extremely successful as a general purpose classification and regression method. The approach, which combines several randomized decision trees and aggregates their predictions by averaging, has shown excellent performance in settings where the number of variables is much larger than the(More)
Random forests are a learning algorithm proposed by Breiman [Mach. Learn. 45 (2001) 5–32] that combines several randomized decision trees and aggregates their predictions by averaging. Despite its wide usage and outstanding practical performance, little is known about the mathematical properties of the procedure. This disparity between theory and practice(More)
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