小结：
1、每颗分类树给出一个分类结果，最多的分类结果做为树组成的森林的结果；
https://mp.weixin.qq.com/s/tjYjDx1MzHAY9GD8yMGoYg
该团队利用 1816 年到 2015 年的数据建立了一个称之为随机森林的统计机器学习模型。该模型通过查看以往的数据以找出案件特征和判决结果之间的联系。模型预测的判决结果包括是否撤销地方法院的裁决以及每一个法官是如何判决的。然后模型通过查看该年份每一个案例的特征预测可能的判决结果。最后，该算法会学习预测的结果，这样以更新以后判决的策略。
Random forests - classification description https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
Overview
We assume that the user knows about the construction of single classification trees. Random Forests grows many classification trees. To classify a new object from an input vector, put the input vector down each of the trees in the forest. Each tree gives a classification, and we say the tree "votes" for that class. The forest chooses the classification having the most votes (over all the trees in the forest).
RF is an example of a tool that is useful in doing analyses of scientific data.
But the cleverest algorithms are no substitute for human intelligence and knowledge of the data in the problem.
Take the output of random forests not as absolute truth, but as smart computer generated guesses that may be helpful in leading to a deeper understanding of the problem.