在机器学习中，随机森林是一个包含多个决策树的分类器， 并且其输出的类别是由个别树输出的类别的众数而定。 Leo Breiman和Adele Cutler发展出推论出随机森林的算法。 而 "Random Forests" 是他们的商标。 这个术语是1995年由贝尔实验室的Tin Kam Ho所提出的随机决策森林(random decision forests)而来的。这个方法则是结合 Breimans 的 "Bootstrap aggregating" 想法和 Ho 的"random subspace method"" 以建造决策树的集合。(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.)