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    2 papers

    2001Machine Learning, Vol. 45, pp. 5–32, DOI: 10.1023/A:1010933404324

    Random Forests

    Leo Breiman

    Finding: Generalisation error converges as the number of trees grows; the method is robust to noise and also yields variable-importance measures.

    random forestdecision trees
    2001Machine Learning, vol. 45, no. 1, pp. 5-32, DOI: 10.1023/A:1010933404324

    Random Forests

    Leo Breiman

    Finding: Random Forest was shown to outperform single trees and other ensemble methods, handling high-dimensional and noisy data well.

    Random Forestdecision tree
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