决策树算法简单应用

素颜马尾好姑娘i 2022-02-28 10:20 225阅读 0赞

# -*- coding: utf-8 -*-
    
    from sklearn import tree
    
    # visualize code
    from sklearn.externals.six import StringIO
    import pydotplus
    
    # 决策树算法
    # 初步的两个特性的判断，[重量，表皮光滑度](对于水果，可以是：1=光滑，0=粗糙)
    # 结论标签，1=苹果，0=橘子
    features = [[140,1],[130,1],[150,0],[170,0],[150,1],[130,0]]
    features_names = ['重量','表皮光滑度']
    labels   = [0, 0, 1, 1, 0, 1]
    label_name = ['橘子','苹果']
    
    #调用决策树算法的核心语句
    dt = tree.DecisionTreeClassifier()
    dt= dt.fit(features, labels)
    
    #测试数据，预测[200,1]
    print(dt.predict([[200,1]]))
    # 可以根据测试数据，得到预测的结果
    
    # 此处添加三个"截止
    # 移动标记来进行代码运行测试
    dot_data = StringIO()
    tree.export_graphviz(dt,
                            out_file = dot_data,
                            feature_names = features_names,
                            class_names   = label_name,
                            filled   = True, rounded = True,
                            impurity = False)
    graph = pydotplus.graph_from_dot_data(dot_data.getvalue().replace('helvetica','"Microsoft YaHei"'))
    print(dot_data)
    
    #graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
    #下面解决Windows下乱码问题
    graph = pydotplus.graph_from_dot_data(dot_data.getvalue().replace('helvetica','"Microsoft YaHei"'))
    print(graph)
    graph.write_pdf("basic.pdf")

从执行结果可以知道预测\[200,1\]为苹果

runfile('D:/ai/py/tensorflow-basic.py', wdir='D:/ai/py')
    [0]
    <_io.StringIO object at 0x000001D93B47CA68>
    <pydotplus.graphviz.Dot object at 0x000001D93B905F98>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXVxaWFuZw_size_16_color_FFFFFF_t_70]修改数据

features = [[140,1],[130,1],[150,0],[170,0],[150,1],[130,0]]
    features_names = ['重量','表皮光滑度']
    labels   = [0, 0, 1, 0, 0, 1]

则\[200,0\]的预测结果是\[0\]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXVxaWFuZw_size_16_color_FFFFFF_t_70 1]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXVxaWFuZw_size_16_color_FFFFFF_t_70]: /images/20220228/dac0dd40e9b0402581007971b8c03829.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXVxaWFuZw_size_16_color_FFFFFF_t_70 1]: /images/20220228/586065330dda4a149b01141b0c0cc2a7.png