LIANYONGXING. BLOG

# 决策树应用（sklearn）

# 问题说明

对于一个人是否购买电子产品，有age、income、student、credit_rating几个影响因素，根据这些影响因素拟合出一棵决策树，并进行预测。

RID age income student credit_rating class_buys_computer
1 youth high no fair no
2 youth high no excellent no
3 middle_aged high no fair yes
4 senior medium no fair yes
5 senior low yes fair yes
6 senior low yes excellent no
7 middle_aged low yes excellent yes
8 youth medium no fair no
9 youth low yes fair yes
10 senior medium yes fair yes
11 youth medium yes excellent yes
12 middle_aged medium no excellent yes
13 middle_aged high yes fair yes
14 senior medium no excellent no

# PythonCode

# 导入sklearn相关库

import sklearn
import csv
from sklearn.feature_extraction import DictVectorizer
from sklearn import preprocessing
from sklearn import tree

# 导入csv文件，读取原始数据

file = open(r'/Users/lianyongxing/Desktop/machineLearning/decisionTree/src/AllElectronics.csv','rt')
reader = csv.reader(file) #按行读取
headers = next(reader)

featureList = []
labelList = []
for row in reader:
    labelList.append(row[-1])
    rowDict = {}
    for i in range(1, len(headers) - 1):
        rowDict[headers[i]] = row[i]

# 数据格式转化

# 字典的向量化
# 这个过程将原始数据中的属性值youth、high、no等数据转化为sklearn可识别的0、1；将原始数据中的标签yes、no转化为sklearn可识别的0、1
vec = DictVectorizer()
dummyX = vec.fit_transform(featureList).toarray()
# # print(dummyX)
# 
dummyY = preprocessing.label_binarize(labelList,classes=['no','yes'])
# # print(dummyY)

# 构造决策树

# 根据数据构造决策树
clf = tree.DecisionTreeClassifier(criterion='entropy')
clf = clf.fit(dummyX, dummyY)

# 输出结果

# 将输出的结果保存为dot文件，后续用graphviz程序打开
with open("output.dot",'w') as f:
    f = tree.export_graphviz(clf, feature_names=vec.get_feature_names(), out_file= f)

# 可视化

# 在终端中将dot文件转换为可视化的pdf文件
dot -T pdf output.dot -o output.pdf

# 结果

得到的决策树如下所示

c4d0d37beb671b662b11de8aaaa17698

从中可以直观看出每一层的熵值和分类情况

# 预测

之后根据拟合好的决策树对新数据进行预测

# 在这里取了原始数据的第一行，然后把第一列和低4列的值修改了一下，作为新的输入
# 然后用决策树进行预测，得到predictY 
oneRowX = dummyX[0, :]
print("oneRowX:" + str(oneRowX))

newRowX = oneRowX
newRowX[0] = 1
newRowX[3] = 0

predictedY = clf.predict([newRowX])

← 聚类 →