决策树 实现

决策树实现 通过预处理基础数据 然后通过tree里的决策树方法实现二分类问题其中
AllElectronics.csv 是一个excel表格字段对应数据



#encoding=utf-8from sklearn.feature_extraction import DictVectorizerimport csvfrom sklearn import treefrom sklearn import preprocessingfrom sklearn.externals.six import StringIOallElectronicsData = open(r'AllElectronics.csv','rb')reader = csv.reader(allElectronicsData)headers = reader.next()featureList = []labelList=[]for row in reader:    labelList.append(row[len(row)-1])    rowDict={}    for i in range(1, len(row)-1):        rowDict[headers[i]] = row[i]    featureList.append(rowDict)vec = DictVectorizer()dummyX = vec.fit_transform(featureList).toarray()lb = preprocessing.LabelBinarizer()dummyY = lb.fit_transform(labelList)clf = tree.DecisionTreeClassifier(criterion='entropy')clf = clf.fit(dummyX, dummyY)print(str(clf))