决策树、SVM、随机森林在评定信用等级上的应用

2024-01-10 21:50
文章标签 应用 决策树 随机 森林 svm 评定 信用等级

本文主要是介绍决策树、SVM、随机森林在评定信用等级上的应用，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

以下为我们这次的数据集信息，分别是各类特征和信用评定Label，属于二分类问题。

本文章想通过比较决策树、SVM和随机森林在该数据集上的表现

在这里插入图片描述

导入数据，查看缺失值

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

data =pd.read_excel('./GermanCredit.xls', sheet_name='Data')  #读取xls文件的Data sheet
data.head()

num_features = ['DURATION','AMOUNT','INSTALL_RATE','AGE','NUM_CREDITS','NUM_DEPENDENTS']
cat_features = data.columns.drop(num_features + ['OBS#'])

data.isnull().sum()
# 都没有缺失值

OBS#                0
CHK_ACCT            0
DURATION            0
HISTORY             0
NEW_CAR             0
USED_CAR            0
FURNITURE           0
RADIO/TV            0
EDUCATION           0
RETRAINING          0
AMOUNT              0
SAV_ACCT            0
EMPLOYMENT          0
INSTALL_RATE        0
MALE_DIV            0
MALE_SINGLE         0
MALE_MAR_or_WID     0
CO-APPLICANT        0
GUARANTOR           0
PRESENT_RESIDENT    0
REAL_ESTATE         0
PROP_UNKN_NONE      0
AGE                 0
OTHER_INSTALL       0
RENT                0
OWN_RES             0
NUM_CREDITS         0
JOB                 0
NUM_DEPENDENTS      0
TELEPHONE           0
FOREIGN             0
RESPONSE            0
dtype: int64

将连续特征离散化

发现DURATION是贷款期限，分布在4-72个月之间，而且分布是一个看似左偏的正态分布，做一个hist图看得更清楚！

plt.hist(data['DURATION'])

在这里插入图片描述

(array([171., 262., 337.,  57.,  86.,  17.,  54.,   2.,  13.,   1.]),array([ 4. , 10.8, 17.6, 24.4, 31.2, 38. , 44.8, 51.6, 58.4, 65.2, 72. ]),<a list of 10 Patch objects>)

取五分位数，将DURATION特征转化成cat_features做离散化处理

x<20 dua_rank = 1
20<x<40 dua_rank = 2
40<x<60 dua_rank = 3
60<x<72 dua_rank = 4
并且创造一个新特征 dua_rank 添加在new_data中,也可以用sklearn.KBinsDiscretizer进行分箱处理

dua_rank = []
duration = data['DURATION']
for i in duration:if i <=20:dua_rank.append(1)elif i<= 40:dua_rank.append(2)elif i < 60:dua_rank.append(3)else:dua_rank.append(4)

可以看出，大部分的duration分布在rank1、2的区间内

plt.hist(dua_rank,bins = 4)

(array([554., 365.,  67.,  14.]),array([1.  , 1.75, 2.5 , 3.25, 4.  ]),<a list of 4 Patch objects>)

在这里插入图片描述

new_data = data.copy()
new_data['dua_rank'] = dua_rank

new_data.head()
OBS#CHK_ACCTDURATIONHISTORYNEW_CARUSED_CARFURNITURERADIO/TVEDUCATIONRETRAINING...OTHER_INSTALLRENTOWN_RESNUM_CREDITSJOBNUM_DEPENDENTSTELEPHONEFOREIGNRESPONSEdua_rank
01064000100...0012211011
121482000100...0011210003
233124000010...0011120011
340422001000...0001220013
450243100000...0002220002

5 rows × 33 columns

plt.hist(data['AMOUNT'])

(array([445., 293.,  97.,  80.,  38.,  19.,  14.,   8.,   5.,   1.]),array([  250. ,  2067.4,  3884.8,  5702.2,  7519.6,  9337. , 11154.4,12971.8, 14789.2, 16606.6, 18424. ]),<a list of 10 Patch objects>)

在这里插入图片描述

我们也将AMOUNT特征分为1-10级，用十分位点作为评分标准

同样可以用sklearn.KBinsDiscretizer进行分箱离散化

percent = np.percentile(data['AMOUNT'], [i * 10 for i in range(1,10)])

amount_rank = []
for i in data['AMOUNT']:if i < percent[0]:amount_rank.append(1)elif i < percent[1]:amount_rank.append(2)elif i <percent[2]:amount_rank.append(3)elif i < percent[3]:amount_rank.append(4)elif i < percent[4]:amount_rank.append(5)elif i < percent[5]:amount_rank.append(6)elif i < percent[6]:amount_rank.append(7)elif i < percent[7]:amount_rank.append(8)elif i < percent[8]:amount_rank.append(9)else:amount_rank.append(10)
new_data['amount_rank'] = amount_rank

data['INSTALL_RATE'].value_counts()

4    476
2    231
3    157
1    136
Name: INSTALL_RATE, dtype: int64
INSTSLL_RATE 分期付款率占可支配收入的百分比可以直接看作一个离散变量,不作处理
AGE 变量做离散化处理，原理同上的特征处理
data['AGE'].describe()

count    1000.000000
mean       35.546000
std        11.375469
min        19.000000
25%        27.000000
50%        33.000000
75%        42.000000
max        75.000000
Name: AGE, dtype: float64

percent = np.percentile(data['AGE'], [25, 50, 75])
age_rank = []
for i in data['AGE']:if i <= percent[0]:age_rank.append(1)elif i <= percent[1]:age_rank.append(2)elif i <= percent[2]:age_rank.append(3)else:age_rank.append(4)
new_data['age_rank'] = age_rank      

new_data.head()
OBS#CHK_ACCTDURATIONHISTORYNEW_CARUSED_CARFURNITURERADIO/TVEDUCATIONRETRAINING...OWN_RESNUM_CREDITSJOBNUM_DEPENDENTSTELEPHONEFOREIGNRESPONSEdua_rankamount_rankage_rank
01064000100...1221101124
121482000100...1121000391
233124000010...1112001154
340422001000...01220013104
450243100000...0222000294

5 rows × 35 columns

data['NUM_CREDITS'].describe()

count    1000.000000
mean        1.407000
std         0.577654
min         1.000000
25%         1.000000
50%         1.000000
75%         2.000000
max         4.000000
Name: NUM_CREDITS, dtype: float64
num_credits 表示持有的信用卡的数目，范围在1-3 ，也可以不用处理
plt.hist(data['NUM_CREDITS'],bins = 4)

(array([633., 333.,  28.,   6.]),array([1.  , 1.75, 2.5 , 3.25, 4.  ]),<a list of 4 Patch objects>)

在这里插入图片描述

data['NUM_DEPENDENTS'].describe()

count    1000.000000
mean        1.155000
std         0.362086
min         1.000000
25%         1.000000
50%         1.000000
75%         1.000000
max         2.000000
Name: NUM_DEPENDENTS, dtype: float64
也只有两个类， 我也不需要处理
plt.hist(data['NUM_DEPENDENTS'],bins = 2)

(array([845., 155.]), array([1. , 1.5, 2. ]), <a list of 2 Patch objects>)

在这里插入图片描述

将刚刚做处理的num_features 删除掉,用rank特征代替
new_data.drop(['AGE','AMOUNT','DURATION'], axis = 1,inplace=True)
将不具有大小关系的特征进行one-hot encoding 以消除其大小的含义

所以我们将 HISTORY , JOB 特征 进行独热编码

history = data['HISTORY']
new_history = pd.get_dummies(history,prefix='histor')
job = data['JOB']
new_job = pd.get_dummies(job, prefix= 'job')
new_data = pd.concat([new_data, new_history, new_job], axis = 1)
new_data.drop(['HISTORY', 'JOB'], axis = 1,inplace=True)

new_data
OBS#CHK_ACCTNEW_CARUSED_CARFURNITURERADIO/TVEDUCATIONRETRAININGSAV_ACCTEMPLOYMENT...age_rankhistor_0histor_1histor_2histor_3histor_4job_0job_1job_2job_3
01000010044...4000010010
12100010002...1001000010
23300001003...4000010100
34000100003...4001000010
45010000002...4000100010
..................................................................
995996300100003...2001000100
996997001000002...3001000001
997998300010004...3001000010
998999000010002...1001000010
9991000101000010...1000010010

1000 rows × 39 columns

new_data.info

<bound method DataFrame.info of      OBS#  CHK_ACCT  NEW_CAR  USED_CAR  FURNITURE  RADIO/TV  EDUCATION  \
0       1         0        0         0          0         1          0   
1       2         1        0         0          0         1          0   
2       3         3        0         0          0         0          1   
3       4         0        0         0          1         0          0   
4       5         0        1         0          0         0          0   
..    ...       ...      ...       ...        ...       ...        ...   
995   996         3        0         0          1         0          0   
996   997         0        0         1          0         0          0   
997   998         3        0         0          0         1          0   
998   999         0        0         0          0         1          0   
999  1000         1        0         1          0         0          0   RETRAINING  SAV_ACCT  EMPLOYMENT  ...  age_rank  histor_0  histor_1  \
0             0         4           4  ...         4         0         0   
1             0         0           2  ...         1         0         0   
2             0         0           3  ...         4         0         0   
3             0         0           3  ...         4         0         0   
4             0         0           2  ...         4         0         0   
..          ...       ...         ...  ...       ...       ...       ...   
995           0         0           3  ...         2         0         0   
996           0         0           2  ...         3         0         0   
997           0         0           4  ...         3         0         0   
998           0         0           2  ...         1         0         0   
999           0         1           0  ...         1         0         0   histor_2  histor_3  histor_4  job_0  job_1  job_2  job_3  
0           0         0         1      0      0      1      0  
1           1         0         0      0      0      1      0  
2           0         0         1      0      1      0      0  
3           1         0         0      0      0      1      0  
4           0         1         0      0      0      1      0  
..        ...       ...       ...    ...    ...    ...    ...  
995         1         0         0      0      1      0      0  
996         1         0         0      0      0      0      1  
997         1         0         0      0      0      1      0  
998         1         0         0      0      0      1      0  
999         0         0         1      0      0      1      0  [1000 rows x 39 columns]>

from sklearn import tree
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV
import graphviz
from sklearn.svm import SVC

x = new_data.drop(['RESPONSE'], axis = 1)
y = new_data.loc[:,['RESPONSE']]
train_x, test_x, train_y, test_y = train_test_split(x, y, test_size = 0.2, random_state = 777)

决策树分类器

采用GridSearchCV进行可选参数的遍历，选出一个最优模型，以后的决策树，SVM，随机森林都采用这个方法进行遍历调参，选出最优参数和最优模型

model = DecisionTreeClassifier(criterion='gini',max_depth=4,min_samples_split=4,max_features=6)
params = {'criterion':['gini','entropy'],'max_depth': range(1,30),'min_samples_split': range(2,10),'min_samples_leaf' : range(1,6),
}
cv = GridSearchCV(model,param_grid= params,n_jobs= -1,verbose=1,scoring='accuracy', cv = 5)
cv.fit(data.iloc[:,:-1], data.iloc[:,-1])

Fitting 5 folds for each of 2320 candidates, totalling 11600 fits[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done 1348 tasks      | elapsed:    2.4s
[Parallel(n_jobs=-1)]: Done 8248 tasks      | elapsed:   15.7s
[Parallel(n_jobs=-1)]: Done 11600 out of 11600 | elapsed:   22.5s finishedGridSearchCV(cv=5, error_score='raise-deprecating',estimator=DecisionTreeClassifier(class_weight=None,criterion='gini', max_depth=4,max_features=6,max_leaf_nodes=None,min_impurity_decrease=0.0,min_impurity_split=None,min_samples_leaf=1,min_samples_split=4,min_weight_fraction_leaf=0.0,presort=False, random_state=None,splitter='best'),iid='warn', n_jobs=-1,param_grid={'criterion': ['gini', 'entropy'],'max_depth': range(1, 30),'min_samples_leaf': range(1, 6),'min_samples_split': range(2, 10)},pre_dispatch='2*n_jobs', refit=True, return_train_score=False,scoring='accuracy', verbose=1)

model1 = cv.best_estimator_
cv.best_params_,cv.best_score_

({'criterion': 'gini','max_depth': 5,'min_samples_leaf': 2,'min_samples_split': 5},0.734)

影响决策树决策的特征重要性的可视化

发现最重要的特征为 CHK_ACCT、DURATION、AGE、HISTORY
plt.figure(figsize= (9,6))
plt.bar(data.iloc[:,:-1].columns, model1.feature_importances_)
plt.xticks(rotation = 90)
plt.show()

在这里插入图片描述

cv.fit(x, y)
model2 = cv.best_estimator_
cv.best_params_,cv.best_score_

Fitting 5 folds for each of 2320 candidates, totalling 11600 fits[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done 1328 tasks      | elapsed:    3.0s
[Parallel(n_jobs=-1)]: Done 7928 tasks      | elapsed:   18.5s
[Parallel(n_jobs=-1)]: Done 11600 out of 11600 | elapsed:   27.5s finished({'criterion': 'gini','max_depth': 5,'min_samples_leaf': 2,'min_samples_split': 2},0.728)

model2

DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,max_features=6, max_leaf_nodes=None,min_impurity_decrease=0.0, min_impurity_split=None,min_samples_leaf=2, min_samples_split=2,min_weight_fraction_leaf=0.0, presort=False,random_state=None, splitter='best')

将预处理过的特征 进行决策树分析，发现 CHK_ACCT仍是最重要的影响特征，其他特征并没有表现出来，模型的表现也不如未处理的数据

plt.figure(figsize= (9,6))
plt.bar(x.columns, model2.feature_importances_)
plt.xticks(rotation = 90)
plt.show()

在这里插入图片描述

决策树的可视化

graph_data = tree.export_graphviz(model2,out_file = None,feature_names=x.columns,filled= True, rounded= True)
graph = graphviz.Source(graph_data,)
graph

在这里插入图片描述

SVM分类器

可以看出SVM在全是离散型变量的数据集的预测上表现的并不是很好，不如决策树，accuracy 在为预处理的数据集和 经过离散处理的数据集上都只有0.7和0.65的表现
model = SVC()
params = {'C':range(1,10)}
cv = GridSearchCV(model,param_grid=params, verbose = 1,cv = 5,scoring='accuracy',n_jobs=-1)
cv.fit(x, y)
model1 = cv.best_estimator_
cv.best_score_

Fitting 5 folds for each of 9 candidates, totalling 45 fits[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done  38 out of  45 | elapsed:    1.3s remaining:    0.2s
[Parallel(n_jobs=-1)]: Done  45 out of  45 | elapsed:    1.5s finished
F:\Anaconda3\lib\site-packages\sklearn\utils\validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().y = column_or_1d(y, warn=True)
F:\Anaconda3\lib\site-packages\sklearn\svm\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning."avoid this warning.", FutureWarning)0.653

cv.fit(data.iloc[:,:-1], data.iloc[:,-1])
model2 = cv.best_estimator_
cv.best_score_

[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.Fitting 5 folds for each of 9 candidates, totalling 45 fits[Parallel(n_jobs=-1)]: Done  38 out of  45 | elapsed:    1.6s remaining:    0.2s
[Parallel(n_jobs=-1)]: Done  45 out of  45 | elapsed:    1.8s finished
F:\Anaconda3\lib\site-packages\sklearn\svm\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning."avoid this warning.", FutureWarning)0.7

随机森林分类器

可以看出集成类分类器会有更好的表现，在预处理的数据集上表现为0.759，未处理的数据集上表现为 0.773
from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(n_estimators=500, random_state=2)
params = {'n_estimators':range(1,1000)
}
cv = GridSearchCV(model, param_grid=params ,verbose = 1,n_jobs=-1, scoring='accuracy')
cv.fit(x,y)
rfc1= cv.best_estimator_
F:\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.warnings.warn(CV_WARNING, FutureWarning)
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.Fitting 3 folds for each of 999 candidates, totalling 2997 fits[Parallel(n_jobs=-1)]: Done  75 tasks      | elapsed:    3.7s
[Parallel(n_jobs=-1)]: Done 526 tasks      | elapsed:   30.7s
[Parallel(n_jobs=-1)]: Done 776 tasks      | elapsed:  1.1min
[Parallel(n_jobs=-1)]: Done 1126 tasks      | elapsed:  2.3min
[Parallel(n_jobs=-1)]: Done 1576 tasks      | elapsed:  4.5min
[Parallel(n_jobs=-1)]: Done 2126 tasks      | elapsed:  8.2min
[Parallel(n_jobs=-1)]: Done 2776 tasks      | elapsed: 14.1min
[Parallel(n_jobs=-1)]: Done 2997 out of 2997 | elapsed: 16.4min finished
F:\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py:715: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().self.best_estimator_.fit(X, y, **fit_params)

rfc = cv.best_estimator_
rfc

RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',max_depth=None, max_features='auto', max_leaf_nodes=None,min_impurity_decrease=0.0, min_impurity_split=None,min_samples_leaf=1, min_samples_split=2,min_weight_fraction_leaf=0.0, n_estimators=673,n_jobs=None, oob_score=False, random_state=2, verbose=0,warm_start=False)

cv.best_score_

0.759

cv.fit(data.iloc[:,:-1],y.iloc[:,-1])
rfc2= cv.best_estimator_

F:\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.warnings.warn(CV_WARNING, FutureWarning)
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.Fitting 3 folds for each of 999 candidates, totalling 2997 fits[Parallel(n_jobs=-1)]: Done  75 tasks      | elapsed:    4.0s
[Parallel(n_jobs=-1)]: Done 423 tasks      | elapsed:   21.4s
[Parallel(n_jobs=-1)]: Done 673 tasks      | elapsed:   49.5s
[Parallel(n_jobs=-1)]: Done 1023 tasks      | elapsed:  1.9min
[Parallel(n_jobs=-1)]: Done 1473 tasks      | elapsed:  4.0min
[Parallel(n_jobs=-1)]: Done 2023 tasks      | elapsed:  7.5min
[Parallel(n_jobs=-1)]: Done 2673 tasks      | elapsed: 13.2min
[Parallel(n_jobs=-1)]: Done 2997 out of 2997 | elapsed: 16.5min finished

cv.best_score_ #

0.773

总结

1、在决策树，随机森林，SVM上，经过预处理的数据反而准确率不及源数据，可能造成的原因是，处理后将原数据的某些特点抹掉了，使模型欠拟合
2、SVM在大部分特征是0、1特征的数据集上表现不如树模型
3、随机森林这类集成模型表现好于单模型，但需要计算资源较多，耗时长。
4、影响贷款最重要的特征是CHK_ACCT，即支票帐户状态。
5、随机森林和决策树这种树类模型在实际应用中更具可解释性。

总体变现并不是很好，之后想再试一试 xgboost 和 LightGBM这类boosting集成树模型

这篇关于决策树、SVM、随机森林在评定信用等级上的应用的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！


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《PostgreSQL的扩展dict_int应用案例解析》dict_int扩展为PostgreSQL提供了专业的整数文本处理能力,特别适合需要精确处理数字内容的搜索场景,本文给大家介绍PostgreS... 目录PostgreSQL的扩展dict_int一、扩展概述二、核心功能三、安装与启用四、字典配置方法
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Python中re模块结合正则表达式的实际应用案例

Python中re模块结合正则表达式的实际应用案例

《Python中re模块结合正则表达式的实际应用案例》Python中的re模块是用于处理正则表达式的强大工具,正则表达式是一种用来匹配字符串的模式,它可以在文本中搜索和匹配特定的字符串模式,这篇文章主... 目录前言re模块常用函数一、查看文本中是否包含 A 或 B 字符串二、替换多个关键词为统一格式三、提
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Java MQTT实战应用

Java MQTT实战应用

《JavaMQTT实战应用》本文详解MQTT协议,涵盖其发布/订阅机制、低功耗高效特性、三种服务质量等级（QoS0/1/2）,以及客户端、代理、主题的核心概念,最后提供Linux部署教程、Sprin... 目录一、MQTT协议二、MQTT优点三、三种服务质量等级四、客户端、代理、主题1. 客户端（Clien
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CSS中的Static、Relative、Absolute、Fixed、Sticky的应用与详细对比

CSS中的Static、Relative、Absolute、Fixed、Sticky的应用与详细对比

《CSS中的Static、Relative、Absolute、Fixed、Sticky的应用与详细对比》CSS中的position属性用于控制元素的定位方式，不同的定位方式会影响元素在页面中的布... css 中的 position 属性用于控制元素的定位方式，不同的定位方式会影响元素在页面中的布局和层叠关
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SpringBoot3应用中集成和使用Spring Retry的实践记录

SpringBoot3应用中集成和使用Spring Retry的实践记录

《SpringBoot3应用中集成和使用SpringRetry的实践记录》SpringRetry为SpringBoot3提供重试机制,支持注解和编程式两种方式,可配置重试策略与监听器,适用于临时性故... 目录1. 简介2. 环境准备3. 使用方式3.1 注解方式 基础使用自定义重试策略失败恢复机制注意事项
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Python使用Tkinter打造一个完整的桌面应用

Python使用Tkinter打造一个完整的桌面应用

《Python使用Tkinter打造一个完整的桌面应用》在Python生态中,Tkinter就像一把瑞士军刀,它没有花哨的特效,却能快速搭建出实用的图形界面,作为Python自带的标准库,无需安装即可... 目录一、界面搭建：像搭积木一样组合控件二、菜单系统：给应用装上“控制中枢”三、事件驱动：让界面“活”
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