随机森林
代码说明:
说明: 用N来表示训练用例(样本)的个数,M表示特征数目。 输入特征数目m,用于确定决策树上一个节点的决策结果;其中m应远小于M。 从N个训练用例(样本)中以有放回抽样的方式,取样N次,形成一个训练集(即bootstrap取样),并用未抽到的用例(样本)作预测,评估其误差。 对于每一个节点,随机选择m个特征,决策树上每个节点的决定都是基于这些特征确定的。根据这m个特征,计算其最佳的分裂方式。 每棵树都会完整成长而不会剪枝,这有可能在建完一棵正常树状分类器后会被采用)。(N is used to represent the number of training cases (samples), and M is used to represent the number of features. The number of input features m is used to determine the decision result of a node in the decision tree, where m should be far less than m. From N training cases (samples), n times are sampled in the way of put back sampling to form a training set (i.e. bootstrap sampling), and the unselected cases (samples) are used to predict and evaluate the error. For each node, m features are randomly selected, and the decision of each node in the decision tree is determined based on these features. According to these m characteristics, the best splitting mode is calculated. Each tree will grow completely without pruning, which may be adopted after building a normal tree classifier).)
文件列表:
教室.jfif, 75832 , 2021-01-28
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