单变量的决策树算法造成树的规模庞大,规则复杂,不易理解。本文结合粗糙集原理中的相对核及加权粗糙 度的方法,提出了一种新的多变量决策树算法。 -Decision Tree Algorithm in univariate tests caused large-scale, complex rules that are difficult to understand. Based on the rough sets theory of attributes reduction, the c

实现基本功能的决策树分类算法，可以对连续变量进行处理，能显示生成的决策树。-Basic functions of decision tree classification algorithm for continuous variables can be processed to show the resulting decision tree.

实现决策树算法 深度学习 。。。。。。。。。。。(decision tree learning)