This article is about the machine learning technique. For other kinds of random tree, see Random tree.
Binary search tree based ensemble machine learning method
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Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the mean or average prediction of the individual trees is returned.[1][2] Random decision forests correct for decision trees' habit of overfitting to their training set.[3]: 587–588
The first algorithm for random decision forests was created in 1995 by Tin Kam Ho[1] using the random subspace method,[2] which, in Ho's formulation, is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg.[4][5][6]
An extension of the algorithm was developed by Leo Breiman[7] and Adele Cutler,[8] who registered[9] "Random Forests" as a trademark in 2006 (as of 2019[update], owned by Minitab, Inc.).[10] The extension combines Breiman's "bagging" idea and random selection of features, introduced first by Ho[1] and later independently by Amit and Geman[11] in order to construct a collection of decision trees with controlled variance.
^ abcHo, Tin Kam (1995). Random Decision Forests(PDF). Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14–16 August 1995. pp. 278–282. Archived from the original (PDF) on 17 April 2016. Retrieved 5 June 2016.
^ abHo TK (1998). "The Random Subspace Method for Constructing Decision Forests" (PDF). IEEE Transactions on Pattern Analysis and Machine Intelligence. 20 (8): 832–844. doi:10.1109/34.709601. S2CID 206420153.
^Cite error: The named reference elemstatlearn was invoked but never defined (see the help page).
^Kleinberg E (1990). "Stochastic Discrimination" (PDF). Annals of Mathematics and Artificial Intelligence. 1 (1–4): 207–239. CiteSeerX 10.1.1.25.6750. doi:10.1007/BF01531079. S2CID 206795835. Archived from the original (PDF) on 2018-01-18.
^Kleinberg E (1996). "An Overtraining-Resistant Stochastic Modeling Method for Pattern Recognition". Annals of Statistics. 24 (6): 2319–2349. doi:10.1214/aos/1032181157. MR 1425956.
^Kleinberg E (2000). "On the Algorithmic Implementation of Stochastic Discrimination" (PDF). IEEE Transactions on Pattern Analysis and Machine Intelligence. 22 (5): 473–490. CiteSeerX 10.1.1.33.4131. doi:10.1109/34.857004. S2CID 3563126. Archived from the original (PDF) on 2018-01-18.
^Cite error: The named reference rpackage was invoked but never defined (see the help page).
^U.S. trademark registration number 3185828, registered 2006/12/19.
^"RANDOM FORESTS Trademark of Health Care Productivity, Inc. - Registration Number 3185828 - Serial Number 78642027 :: Justia Trademarks".
^Amit Y, Geman D (1997). "Shape quantization and recognition with randomized trees" (PDF). Neural Computation. 9 (7): 1545–1588. CiteSeerX 10.1.1.57.6069. doi:10.1162/neco.1997.9.7.1545. S2CID 12470146. Archived from the original (PDF) on 2018-02-05. Retrieved 2008-04-01.
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Executes all calculations on the GPU # Create a tensor and fill it with random numbers a = torch.randn(2, 3, device=device, dtype=dtype) print(a) # Output:...
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