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README.md	https://github.com/ChromatinRemodeling/Decision_tree-python/blob/master/README.md
dataset.jpg	https://github.com/ChromatinRemodeling/Decision_tree-python/blob/master/dataset.jpg
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tree.py	https://github.com/ChromatinRemodeling/Decision_tree-python/blob/master/tree.py
tree.py	https://github.com/ChromatinRemodeling/Decision_tree-python/blob/master/tree.py
treePlotter.py	https://github.com/ChromatinRemodeling/Decision_tree-python/blob/master/treePlotter.py
treePlotter.py	https://github.com/ChromatinRemodeling/Decision_tree-python/blob/master/treePlotter.py
数据表.jpg	https://github.com/ChromatinRemodeling/Decision_tree-python/blob/master/%E6%95%B0%E6%8D%AE%E8%A1%A8.jpg
数据表.jpg	https://github.com/ChromatinRemodeling/Decision_tree-python/blob/master/%E6%95%B0%E6%8D%AE%E8%A1%A8.jpg
数据表.xlsx	https://github.com/ChromatinRemodeling/Decision_tree-python/blob/master/%E6%95%B0%E6%8D%AE%E8%A1%A8.xlsx
数据表.xlsx	https://github.com/ChromatinRemodeling/Decision_tree-python/blob/master/%E6%95%B0%E6%8D%AE%E8%A1%A8.xlsx
README	https://github.com/ChromatinRemodeling/Decision_tree-python
	https://github.com/ChromatinRemodeling/Decision_tree-python#decision_tree-python
	https://github.com/ChromatinRemodeling/Decision_tree-python#决策树分类id3c45cart
	https://github.com/ChromatinRemodeling/Decision_tree-python#三种算法的区别如下
	https://github.com/ChromatinRemodeling/Decision_tree-python#1-id3算法以信息增益为准则来进行选择划分属性选择信息增益最大的
	https://github.com/ChromatinRemodeling/Decision_tree-python#2-c45算法先从候选划分属性中找出信息增益高于平均水平的属性再从中选择增益率最高的
	https://github.com/ChromatinRemodeling/Decision_tree-python#3-cart算法使用基尼指数来选择划分属性选择基尼值最小的属性作为划分属性
	https://github.com/ChromatinRemodeling/Decision_tree-python#本次实验我的数据集如下所示
	https://github.com/ChromatinRemodeling/Decision_tree-python#共分为四个属性特征年龄段有工作有自己的房子信贷情况
	https://github.com/ChromatinRemodeling/Decision_tree-python#现根据这四种属性特征来决定是否给予贷款
	https://github.com/Erikfather/Decision_tree-python/blob/master/%E6%95%B0%E6%8D%AE%E8%A1%A8.jpg
	https://github.com/ChromatinRemodeling/Decision_tree-python#为了方便我对数据集进行如下处理
	https://github.com/ChromatinRemodeling/Decision_tree-python#在编写代码之前我们先对数据集进行属性标注
	https://github.com/ChromatinRemodeling/Decision_tree-python#0年龄0代表青年1代表中年2代表老年
	https://github.com/ChromatinRemodeling/Decision_tree-python#1有工作0代表否1代表是
	https://github.com/ChromatinRemodeling/Decision_tree-python#2有自己的房子0代表否1代表是
	https://github.com/ChromatinRemodeling/Decision_tree-python#3信贷情况0代表一般1代表好2代表非常好
	https://github.com/ChromatinRemodeling/Decision_tree-python#4类别是否给贷款no代表否yes代表是
	https://github.com/ChromatinRemodeling/Decision_tree-python#存入txt文件中
	https://github.com/Erikfather/Decision_tree-python/blob/master/dataset.jpg
	https://github.com/ChromatinRemodeling/Decision_tree-python#然后分别利用id3c45cart三种算法对数据集进行决策树分类
	https://github.com/ChromatinRemodeling/Decision_tree-python#具体代码见treepy和treeplotterpy
	https://github.com/ChromatinRemodeling/Decision_tree-python#实验结果如下
	https://github.com/ChromatinRemodeling/Decision_tree-python#1先将txt中数据读入数组并打印出数据集长度即共有多少条数据并计算出起始信息熵entd然后分别找出三种算法的首个最优特征索引
	https://github.com/Erikfather/Decision_tree-python/blob/master/pic_results/first_find_bset_Index.jpg
	https://github.com/ChromatinRemodeling/Decision_tree-python#2下面通过相应的最优划分属性分别创建相应的决策树并对测试集进行测试测试集如下
	https://github.com/Erikfather/Decision_tree-python/blob/master/testdata.jpg
	https://github.com/ChromatinRemodeling/Decision_tree-python#结果如下
	https://github.com/ChromatinRemodeling/Decision_tree-python#id3算法
	https://github.com/Erikfather/Decision_tree-python/blob/master/pic_results/ID3.jpg
	https://github.com/Erikfather/Decision_tree-python/blob/master/pic_results/figure_ID3.jpg
	https://github.com/ChromatinRemodeling/Decision_tree-python#c45算法
	https://github.com/Erikfather/Decision_tree-python/blob/master/pic_results/C4.5.jpg
	https://github.com/Erikfather/Decision_tree-python/blob/master/pic_results/figure_C4.5.jpg
	https://github.com/ChromatinRemodeling/Decision_tree-python#cart算法
	https://github.com/Erikfather/Decision_tree-python/blob/master/pic_results/CART.jpg
	https://github.com/Erikfather/Decision_tree-python/blob/master/pic_results/figure_CART.jpg
	https://github.com/ChromatinRemodeling/Decision_tree-python#由上面结果可以看出
	https://github.com/ChromatinRemodeling/Decision_tree-python#1id3和c45的最优索引以及决策树形图是相同的而cart的最优索引以及决策树形图与前面两者不同这与它们的选择标准以及训练集有关
	https://github.com/ChromatinRemodeling/Decision_tree-python#2但同时我们也发现三种算法对测试集的测试结果是相同的经过后期手动匹配结果完全正确这说明我们的决策树实验结果是正确的
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