from sklearn.feature_extraction import DictVectorizerfrom sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizerfrom sklearn.preprocessing import MinMaxScaler,StandardScalerfrom sklearn.model_selection import train_test_splitfrom sklearn.tree import DecisionTreeClassifier,export_graphvizimport jieba

import  pandas as pd

调用方法来实现：

def decision():    #获取数据    taitan=pd.read_csv('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt')    #获取特征值    x=taitan[['pclass','age','sex']]    #获取目标值    y=taitan[['survived']]    #缺失值的处理    用年龄的平均值填补    x['age'].fillna(x['age'].mean(),inplace=True)    #分割数据集到训练集和测试集    x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25)    #对数据进行one_hot编码    dict=DictVectorizer(sparse=False)    x_train = dict.fit_transform(x_train.to_dict(orient='records'))    print(dict.get_feature_names())    x_test = dict.fit_transform(x_test.to_dict(orient='records'))    #实例化一个决策树    dec=DecisionTreeClassifier()    dec.fit(x_train,y_train)    print("预测准确率：",dec.score(x_test,y_test))    export_graphviz(dec,out_file='./tree.dot',feature_names=['年龄','pclass=1st', 'pclass=2nd', 'pclass=3rd', '女人', '男人'])    # print(x_train)if __name__ == '__main__':    decision()

 如果想查看决策树的结构，可以使用可视化工具将其转化为图片 graphviz

win安装直接exe就行了，安装完成，需要将bin目录配置到path环境变量中去。即可执行dot命令。

dot  -Tpng tree.dot -o tree.png    #将dot文件转为png格式的图片  必须进入到指定目录下去操作