随机森林
对数据样本及特征随机抽取，进行多个决策树训练，防止过拟合，提高泛化能力
一般随机森林的特点：
1、有放回抽样（所以生成每棵树的时候，实际数据集会有重复），
2、以最优划分分裂
Given a standard training set D of size n, bagging generates m new training sets D_i, each of size n′, by sampling from D uniformly and with replacement. This kind of sample is known as a bootstrap sample. The m models are fitted using the above m bootstrap samples and combined by averaging the output (for regression) or voting (for classification).
ExtraTrees算法多一层随机性，在对连续变量特征选取最优分裂值时，不会计算所有分裂值的效果，来选择分裂特征。
而是对每一个特征，在它的特征取值范围内，随机生成一个split value，再计算看选取哪一个特征来进行分裂。
1、Empirical good default values are max_features=n_features for regression problems, and max_features=sqrt(n_features) for classification tasks (where n_features is the number of features in the data).
2、In addition, note that in random forests, bootstrap samples are used by default (bootstrap=True) while the default strategy for extra-trees is to use the whole dataset (bootstrap=False).