决策树算法

1.理论

2.代码

　　2.1 训练数据

RID,age,income,student,credit_rating,class:buy_computer
1,youth,high,no,fair,no
2,youth,high,no,excellent,no
3,middle_age,high,no,fair,yes
4,senior,medium,no,fair,yes
5,senior,low,yes,fair,yes
6,senior,low,yes,excellent,no
7,middle_age,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_age,medium,no,excellent,yes
13,middle_age,high,yes,fair,yes
14,senior,medium,no,excellent,no

　　2.2 代码

from sklearn.feature_extraction import DictVectorizer
import csv
from sklearn import preprocessing
from sklearn import tree
from sklearn.externals.six import StringIO

#Read Data
Data = open(r'D:lernMLData.csv','r', encoding="utf-8")
reader = csv.reader(Data)
headers = next(reader)#获取第一行，特征

print(headers)

featureList = []
labelList = []

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

print(featureList)

#vectorize features
vec = DictVectorizer()
dummyX=vec.fit_transform(featureList).toarray()
print("dummyX:"+str(dummyX))
print(vec.get_feature_names())

print("labelList:"+str(labelList))

#vectorize class labels
lb= preprocessing.LabelBinarizer()
dummyY=lb.fit_transform(labelList)
print("dummyY:"+str(dummyY))

#using Dicision Tree for classification
clf =tree.DecisionTreeClassifier(criterion='entropy')
clf=clf.fit(dummyX,dummyY)
print("clf:"+str(clf))

#visulize model
with open("DT.dot",'w') as f:
    f= tree.export_graphviz(clf,feature_names=vec.get_feature_names(),out_file=f)

#predict result
oneRowX=dummyX[0,:]
print("oneRowX"+str(oneRowX))

newRowX=oneRowX
newRowX[0]=1
newRowX[2]=0
print("newRowX"+str(newRowX))

predictedY=clf.predict([newRowX])
print("predictedY"+str(predictedY))

　　2.3 结果

digraph Tree {
node [shape=box] ;
0 [label="age=middle_age <= 0.5
entropy = 0.94
samples = 14
value = [5, 9]"] ;
1 [label="student=no <= 0.5
entropy = 1.0
samples = 10
value = [5, 5]"] ;
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
2 [label="credit_rating=excellent <= 0.5
entropy = 0.722
samples = 5
value = [1, 4]"] ;
1 -> 2 ;
3 [label="entropy = 0.0
samples = 3
value = [0, 3]"] ;
2 -> 3 ;
4 [label="income=low <= 0.5
entropy = 1.0
samples = 2
value = [1, 1]"] ;
2 -> 4 ;
5 [label="entropy = 0.0
samples = 1
value = [0, 1]"] ;
4 -> 5 ;
6 [label="entropy = 0.0
samples = 1
value = [1, 0]"] ;
4 -> 6 ;
7 [label="age=youth <= 0.5
entropy = 0.722
samples = 5
value = [4, 1]"] ;
1 -> 7 ;
8 [label="credit_rating=fair <= 0.5
entropy = 1.0
samples = 2
value = [1, 1]"] ;
7 -> 8 ;
9 [label="entropy = 0.0
samples = 1
value = [1, 0]"] ;
8 -> 9 ;
10 [label="entropy = 0.0
samples = 1
value = [0, 1]"] ;
8 -> 10 ;
11 [label="entropy = 0.0
samples = 3
value = [3, 0]"] ;
7 -> 11 ;
12 [label="entropy = 0.0
samples = 4
value = [0, 4]"] ;
0 -> 12 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
}


　　2.3.1 结果可视化：graghviz小工具