Python机器学习-决策树的构建

决策树（ID3算法）

开发环境为anaconda中的spyder，所有库已经默认安装，若使用其它环境需要安装外部库，主要代码如下：

# -*- coding: utf-8 -*-
"""
Created on Tue Aug  1 16:09:50 2017

@author: Administrator
"""

# 用来提供数据转换
from sklearn.feature_extraction import DictVectorizer
# 处理csv文件
import csv
from sklearn import preprocessing

# 决策树算法
from sklearn import tree
from sklearn.externals.six import StringIO

allElectronicsData = open(r'D:/data_com.csv')

read = csv.reader(allElectronicsData)

headers = next(read)




featureList = []
labelList = []
for row in read:
    labelList.append(row[-1])
    
    rowDict = {}
    # 从第二列到倒数第二列
    for i in range(1,len(row)-1):
        
        rowDict[headers[i]] = row[i]
    
    featureList.append(rowDict)

# print(featureList)

# 创建一个dummyX 001：youth 010：sensior 100：middle_aged 等等
vec = DictVectorizer()
dummyX = vec.fit_transform(featureList).toarray()

lb = preprocessing.LabelBinarizer()

dummyY = lb.fit_transform(labelList) 
# print(dummyX)

# print(vec)

# print(dummyY)

clf = tree.DecisionTreeClassifier(criterion = 'entropy')
clf = clf.fit(dummyX,dummyY)

with open('D:/allElectronicInformationGainOri.dot','w') as f:
    f = tree.export_graphviz(clf,feature_names=vec.get_feature_names(),out_file=f)

输出的dot文件可以使用graphvize软件转为PDF，graphvize安装目录中的bin目录放入到环境变量的Path中
使用如下命令

dot -Tpdf  xx.dot -o xx.pdf

运行完结果如下：

决策树的构建.png