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- Other systems
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- Update:
- 2017-07-06
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- liwei
Description: In machine learning, random forest is a multiple decision tree classifier, and its output is composed of plural individual tree categories depending on the output category. Leo, Breiman, and Adele Cutler developed algorithms for reasoning about random forests. And "Random Forests" is their trademark. The term was derived from the Tin decision Ho (random forests) proposed by Baer Kam in 1995. This method combines Breimans's Bootstrap aggregating idea and Ho's random subspace method to build a collection of decision trees.
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