随机森林是由多棵树组成的分类或回归方法。主要思想来源于Bagging算法，Bagging技术思想主要是给定一弱分类器及训练集，让该学习算法训练多轮，每轮的训练集由原始训练集中有放回的随机抽取，大小一般跟原始训练集相当，这样依次训练多个弱分类器，最终的分类由这些弱分类器组合，对于分类问题一般采用多数投票法，对于回归问题一般采用简单平均法。随机森林在bagging的基础上，每个弱分类器都是决策树，决策树的生成过程中中，在属性的选择上增加了依一定概率选择属性，在这些属性中选择最佳属性及分割点，传统做法一般是全部属性中去选择最佳属性，这样随机森林有了样本选择的随机性，属性选择的随机性，这样一来增加了每个分类器的差异性、不稳定性及一定程度上避免每个分类器的过拟合（一般决策树有过拟合现象），由此组合分类器增加了最终的泛化能力。-Random Forest classification or regression trees by the multi-component. The main idea comes　the Bagging algorithm, Bagging technology thinking mainly given a set of weak classifiers and training, so that the learning algorithm to train several rounds, each round of training set by the original training set is randomly selected with replacement, with the original size of the general training set fairly, and in turn train a plurality of weak classifiers, the final classification by the weak classifier combination of these, for the general classification of a majority voting method, commonly used for regression simple average method. Random forests bagging on the basis of each of the weak classifiers are decision trees, decision tree in the generation process, the choice of property on the increase in the probability of selection according to certain attributes, and choose the best attributes of these properties in the split point traditional practice is generally to choose the best of