机器学习五(sklearn决策树——多分类)

痛定思痛。 2022-05-21 02:52 331阅读 0赞

### 1.前言 ###

sklearn决策树分类，采用ID3算法，自带iris数据集(根据草的特征进行分类，有3类，用0、1、2标记)。

### 2.决策树绘制准备 ###

（1）下载安装graphviz  
[https://graphviz.gitlab.io/\_pages/Download/Download\_windows.html][https_graphviz.gitlab.io_pages_Download_Download_windows.html]  
（2）pycharm install graphviz  
File->setting->project(project interpreter)->右侧绿+->查询安装  
（3）决策树方法参数说明  
[http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\#sklearn.tree.DecisionTreeClassifier][http_scikit-learn.org_stable_modules_generated_sklearn.tree.DecisionTreeClassifier.html_sklearn.tree.DecisionTreeClassifier]

### 2.python代码 ###

(1) 执行代码如下tree\_class.py:

from sklearn.datasets import load_iris
    from sklearn import tree
    from sklearn.model_selection import train_test_split
    import graphviz
    import os
    
    def multi_class_tree():
        iris = load_iris()
        x = iris['data']
        y = iris['target']
        dtc = tree.DecisionTreeClassifier(criterion="entropy")
        x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1)
        clf = dtc.fit(x_train, y_train)
        print(clf.predict(x_test))
        print(y_test)
        dot_data = tree.export_graphviz(clf, out_file=None)
        graph = graphviz.Source(dot_data)
        os.environ["PATH"] += os.pathsep + 'F:/Program Files/Graphviz2.38/bin/'
        graph.render("iris", view=True)
    
    multi_class_tree()

### 3.验证结果 ###

（1）测试集结果与预测结果比较  
预测：\[0 1 2 2 2 1 1 1 0 2 1 2 0 1 0\]  
实际：\[0 1 2 2 2 1 1 2 0 2 1 2 0 1 0\]  
（2）决策树  
![这里写图片描述][70]

[https_graphviz.gitlab.io_pages_Download_Download_windows.html]: https://graphviz.gitlab.io/_pages/Download/Download_windows.html
[http_scikit-learn.org_stable_modules_generated_sklearn.tree.DecisionTreeClassifier.html_sklearn.tree.DecisionTreeClassifier]: http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier
[70]: /images/20220521/8b0cd71f13e24902a3a602023f76db12.png