Python은 의사결정 트리 알고리즘을 구현합니다.

from sklearn.feature_extraction import DictVectorizer import csv from sklearn import tree from sklearn import preprocessing from sklearn.externals.six import StringIO # 读取csv数据，并将数据和特征值存入字典和类标签列表 allElectronicsData = open(r'AllElectronics.csv', 'rt') reader = csv.reader(allElectronicsData) headers = next(reader) # 原代码中用的是： # headers = reader.next() # 这句代码应该是之前的版本用的，现在已经更新了没有next这个函数 # print(headers) 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) # print(featureList) # 将特征值矢量化,代表将各种参数进行矢量化 vec = DictVectorizer() dummyX = vec.fit_transform(featureList).toarray() # print("dummyX: " + str(dummyX)) # print(vec.get_feature_names()) # print("labelList: " + str(labelList)) # 将类标签列表矢量化，就是最后的结果 lb = preprocessing.LabelBinarizer() dummyY = lb.fit_transform(labelList) # print("dummyY: " + str(dummyY)) # 使用决策树进行分类 clf = tree.DecisionTreeClassifier() # clf = tree.DecisionTreeClassifier(criterion = 'entropy') clf = clf.fit(dummyX, dummyY) # print("clf: " + str(clf)) # 将模型进行可视化 with open("allElectrionicInformationOri.dot", 'w') as f: f = tree.export_graphviz(clf, feature_names = vec.get_feature_names(), out_file = f) oneRowX = dummyX[0, :] # print("oneRowX: " + str(oneRowX)) # 接下来改变一些数据进行预测 newRowX = oneRowX newRowX[0] = 0 newRowX[1] = 1 print("newRowX: " + str(newRowX)) predictedY = clf.predict(newRowX.reshape(1, -1)) # 预测的结果需要加上后面的reshape(1, -1),不然会 # 报错： # ValueError: Expected 2D array, got 1D array instead: # array=[0. 1. 1. 0. 1. 1. 0. 0. 1. 0.]. # Reshape your data either using array.reshape(-1, 1) # if your data has a single feature or array.reshape(1, -1) if it contains a single sample. print("预测的结果为： " + str(predictedY))