3.2 决策树算法应用

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Python
Python机器学习的库：scikit-learn
2.1：特性：
简单高效的数据挖掘和机器学习分析
对所有用户开放，根据不同需求高度可重用性
基于Numpy, SciPy和matplotlib
开源，商用级别：获得 BSD许可
2.2 覆盖问题领域：
分类（classification), 回归（regression), 聚类（clustering), 降维(dimensionality reduction)
模型选择(model selection), 预处理(preprocessing)
使用用scikit-learn
安装scikit-learn: pip, easy_install, windows installer
安装必要package：numpy， SciPy和matplotlib，可使用Anaconda (包含numpy, scipy等科学计算常用
package）
安装注意问题：Python解释器版本（2.7 or 3.4？）, 32-bit or 64-bit系统
例子：

from sklearn.feature_extraction import DictVectorizerimport csvfrom sklearn import  tree, preprocessingfrom sklearn.externals.six import StringIOimport numpy  as npallElectronicsData=open(r'C://AllElectronics.csv')reader=csv.reader(allElectronicsData)headers=reader.next()print(headers)featrueList=[]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]    featrueList.append(rowDict)print(featrueList)vec=DictVectorizer()dummyX=vec.fit_transform(featrueList).toarray()print("dummyX:"+str(dummyX))print(vec.get_feature_names())print("labelList:"+str(labelList))lb=preprocessing.LabelBinarizer()dummyY=lb.fit_transform(labelList)print("dummyY:"+str(dummyY))clf = tree.DecisionTreeClassifier(criterion="entropy")clf=clf.fit(dummyX,dummyY)print("clf:"+str(clf))with open("allElectronicInformationGainOri.dot",'w') as f:    f=tree.export_graphviz(clf,out_file=f,feature_names=vec.get_feature_names())oneRowX=dummyX[0,:]print("oneRowx:"+str(oneRowX))        newRowX=oneRowXnewRowX[0]=1newRowX[1]=0print("newRowX:"+str(newRowX))predictedY = clf.predict(newRowX)print("predictedY:"+str(predictedY))

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