决策树算法的简单实现,决策树（Decision Tree）是一种简单但是广泛使用的分类器。通过训练数据构建决策树，可以高效的对未知的数据进行分类。决策数有两大优点：1）决策树模型可以读性好，具有描述性，有助于人工分析；2）效率高，决策树只需要一次构建，反复使用，每一次预测的最大计算次数不超过决策树的深度。(A simple implementation of decision tree algorithms, decision tree (Decision Tree) is a simple but widely used classifier. Through training data, a decision tree is constructed to classify the unknown data efficiently. The number of decision-making has two advantages: 1) the decision tree model can read well, with descr iptive help manual analysis; 2) high efficiency, only a single decision tree construction, repeated use, the maximum calculated every time prediction is less than the depth of the decision tree.)