Examples of realization of the decision tree (statistical learning methods (Li Hang)) of the loan

Statistical learning methods (Li Hang) This book is an example-based
caveats:

1. I use pycharm
2. python version 3.7
3. graphviz is a software, in which case the pycharm have Quguan under the net
   was added environment variables may also need to restart the computer after the next complete
4. Lack of Han Han library on the security libraries
5. That is my own data set, hand knock.

Loan application sample data table

ID	age	have a job	They have their own house	Credit conditions	category
1	youth	no	no	general	no
2	youth	no	no	it is good	no
3	youth	Yes	no	it is good	Yes
4	youth	Yes	Yes	general	Yes
5	youth	no	no	general	no
6	middle aged	no	no	general	no
7	middle aged	no	no	it is good	no
8	middle aged	Yes	Yes	it is good	Yes
9	middle aged	no	Yes	very good	Yes
10	middle aged	no	Yes	very good	Yes
11	elderly	no	Yes	very good	Yes
12	elderly	no	Yes	it is good	Yes
13	elderly	Yes	no	it is good	Yes
14	elderly	Yes	no	very good	Yes
15	elderly	no	no	general	no

data set

Feature amount	Show
age	Youth: a middle-aged: Elderly 2: 3
have a job	Are: 1 No: 0
They have their own house	1: No: 0
Credit conditions	General: 1 Good: Very good 2: 3
category	Are: 1 No: 0

dataset=[
[1,0,0,1,0],
[1,0,0,2,0],
[1,1,0,2,1],
[1,1,1,1,1],
[1,0,0,1,0],
[2,0,0,2,0],
[2,0,0,2,0],
[2,1,1,2,1],
[2,0,1,3,1],
[2,0,1,2,1],
[3,0,1,3,1],
[3,0,1,2,1],
[3,1,0,3,1],
[3,1,0,3,1],
[3,0,0,1,0]]

X = [x[0:4] for x in dataset] #取出特征值
print(X)
Y = [y[-1] for y in dataset]#取Y值
print(Y)

The method of seeking a decision tree using decision tree sklearn was determined, and then visualized using graphviz

from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
dataset=[
    [1,0,0,1,0],
    [1,0,0,2,0],
    [1,1,0,2,1],
    [1,1,1,1,1],
    [1,0,0,1,0],
    [2,0,0,2,0],
    [2,0,0,2,0],
    [2,1,1,2,1],
    [2,0,1,3,1],
    [2,0,1,2,1],
    [3,0,1,3,1],
    [3,0,1,2,1],
    [3,1,0,3,1],
    [3,1,0,3,1],
    [3,0,0,1,0]
]
feature =['年龄','没有工作','没有自己的房子','信贷情况']
classname =['不借','借']

X = [x[0:4] for x in dataset]
print(X)
Y = [y[-1] for y in dataset]
print(Y)
tree_clf = DecisionTreeClassifier(max_depth=4)
tree_clf.fit(X, Y)

The above method is not required but visual tree, and on this basis with the following code

export_graphviz(
            tree_clf,
            out_file=("loan.dot"),
            feature_names=feature,
            class_names=classname,
            rounded=True,
            filled=True,

        )

Run the code will generate loan.dot file in this directory
and then into the current directory, execute the following command in pycharm inside the local terminal

dot -Tpng loan.dot -o loan.png

It will generate png images.
My directory as follows

But you will find that there will be Chinese garbled
then you continue to add the following code

import re
# 打开 dot_data.dot，修改 fontname="支持的中文字体"
f = open("./loan.dot", "r+", encoding="utf-8")
open('./Tree_utf8.dot', 'w', encoding="utf-8").write(re.sub(r'fontname=helvetica', 'fontname="Microsoft YaHei"', f.read()))
f.close()

Then take a look at renderings

The entire code is as follows

from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
dataset=[
    [1,0,0,1,0],
    [1,0,0,2,0],
    [1,1,0,2,1],
    [1,1,1,1,1],
    [1,0,0,1,0],
    [2,0,0,2,0],
    [2,0,0,2,0],
    [2,1,1,2,1],
    [2,0,1,3,1],
    [2,0,1,2,1],
    [3,0,1,3,1],
    [3,0,1,2,1],
    [3,1,0,3,1],
    [3,1,0,3,1],
    [3,0,0,1,0]
]
feature =['年龄','没有工作','没有自己的房子','信贷情况']
classname =['不借','借']

X = [x[0:4] for x in dataset]
print(X)
Y = [y[-1] for y in dataset]
print(Y)
tree_clf = DecisionTreeClassifier(max_depth=4)
tree_clf.fit(X, Y)

export_graphviz(
            tree_clf,
            out_file=("loan.dot"),
            feature_names=feature,
            class_names=classname,
            rounded=True,
            filled=True,

        )

import re
# 打开 dot_data.dot，修改 fontname="支持的中文字体"
f = open("./loan.dot", "r+", encoding="utf-8")
open('./Tree_utf8.dot', 'w', encoding="utf-8").write(re.sub(r'fontname=helvetica', 'fontname="Microsoft YaHei"', f.read()))
f.close()


'''
dot -Tpng loan.dot -o loan.png
生成图片
'''