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python代码如下
from sklearn.feature_extraction import DictVectorizer from sklearn import tree
from sklearn import preprocessing import csv import graphviz Dtree =
open('西瓜数据集3.0.csv', 'r') reader = csv.reader(Dtree) """ 色泽 1-3代表 浅白 青绿 乌黑 根蒂
1-3代表 稍蜷 蜷缩 硬挺 敲声 1-3代表 清脆 浊响 沉闷 纹理 1-3代表 清晰 稍糊 模糊 脐部 1-3代表 平坦 稍凹 凹陷 触感 1-2代表
硬滑 软粘 好瓜 1代表 是 0 代表 不是 """ # 获取第一行数据 headers = reader.__next__() print(headers)
# 特征和标签列表 featureList = [] labelList = [] for row in reader:
labelList.append(row[-1]) rowDict = {} for i in range(1, len(row)-3):
rowDict[headers[i]] = row[i] featureList.append(rowDict) print(featureList) #
将特征列表转换为01表示 vec = DictVectorizer() x_data =
vec.fit_transform(featureList).toarray() print("x_data: " + str(x_data)) #
将标签列表转换为01表示 lb = preprocessing.LabelBinarizer() y_data =
lb.fit_transform(labelList) print("y_data: " + str(y_data)) # 创建决策树模型 model =
tree.DecisionTreeClassifier(criterion='entropy') # 输入数据建立模型 model.fit(x_data,
y_data) # 测试 x_test = x_data[0] predict = model.predict(x_test.reshape(1,-1))
print("predict: " + str(predict)) # 导出决策树 dot_data =
tree.export_graphviz(model, out_file=None,
feature_names=vec.get_feature_names(), class_names=lb.classes_, filled=True,
rounded=True, special_characters=True) graph = graphviz.Source(dot_data)
graph.render('Tree')
西瓜数据集3.0数据如下
numbercolour and lustreroot and baseKnockvenationumbilical regiontouchdensity
sugar contentgood
12221310.6970.461
23231310.7440.3761
33221310.6340.2641
42231310.6080.3181
51221310.5560.2151
62121220.4030.2371
73122220.4810.1491
83121210.4370.2111
93132210.6660.0910
102311120.2430.2670
111313110.2450.0570
121223120.3430.0990
132122310.6390.1610
141132310.6570.1980
153121220.360.370
161223110.5930.0420
172232210.7190.1030
结果PDF如下：
随后可以进行预剪枝和后剪枝来使结构变得更加清晰简单。