Python Code for Decision Tree Using sklearn Package

This article shares the source code of a decision tree written in Python for everyone's reference, the specific content is as follows

Because of the need for recent internship, I have rewritten the decision tree using the sklearn package in Python.

Tools:sklearnConvert the dot file to pdf format (to visualize the decision tree) graphviz-2.38After downloading and extracting, add the directory of the bin files to the environment variable

The source code is as follows:

from sklearn.feature_extraction import DictVectorizer
import csv
from sklearn import tree
from sklearn import preprocessing
from sklearn.externals.six import StringIO
from xml.sax.handler import feature_external_ges
from numpy.distutils.fcompiler import dummy_fortran_file
# Read in the csv file and put features into list of dict and list of class label
allElectronicsData = open(r'E:/DeepLearning/resources/AllElectronics.csv', 'rt')
reader = csv.reader(allElectronicsData)
headers = next(reader)
featureList = []
labelList = []
for row in reader:
labelList.append(row[len(row)]-1])
rowDict = {}
# Exclude len(row)-1
for i in range(1, len(row)-1) :
rowDict[headers[i]] = row[i]
featureList.append(rowDict)
print(featureList)
vec = DictVectorizer()
dummX = vec.fit_transform(featureList).toarray()
print(str(dummX))
lb = preprocessing.LabelBinarizer()
dummY = lb.fit_transform(lableList)
print(str(dummY))
#entropy=>ID3
clf = tree.DecisionTreeClassifier(criterion='entropy')
clf = clf.fit(dummX, dummY)
print("clf:")+str(clf)
#Visualize tree
with open("resultTree.dot",'w')as f:
f = tree.export_graphviz(clf, feature_names=vec.get_feature_names(), out_file=f)
#How to view the classification of new data
oneRowX = dummX[0,:]
print("oneRowX:")+str(oneRowX)
newRowX = oneRowX
newRowX[0] = 1
newRowX[2] = 0
predictedY = clf.predict(newRowX)
print("predictedY:")+ str(predictedY)

The AllElectronics.csv here is shown in the format as follows:

Early this morning, I finally managed to install jdk, eclipse, and pydev on linux, but, but, but, when I tried to install numpy, I always got an error, and I found out that it was because I didn't have gcc, so I went to install gcc, which was really frustrating. Now gcc still hasn't been installed successfully, and I need to think of another way

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