ML之LoR：基于信用卡数据集利用LoR逻辑回归算法实现如何开发通用信用风险评分卡模型之以toad框架全流程讲解

ML之LoR：基于信用卡数据集利用LoR逻辑回归算法实现如何开发通用信用风险评分卡模型之以toad框架全流程讲解

目录

基于信用卡数据集利用LoR逻辑回归算法实现如何开发通用信用风险评分卡模型之以toad框架全流程讲解

# 1、定义数据集

# 1.1、加载德国信用卡数据集

#1.2、对各个变量进行EDA分析

# 1.3、输出连续型变量的mean、std、min、3种分位数、max

# 2、数据预处理

# 2.1、对类别型目标变量映射成数值型变量

# 2.2、分析每个特征的iv、基尼系数gini、熵entropy、unique等

# 2.3、筛选特征：分别基于IV、empty、corr指标

# 2.4、分箱处理

# 2.5、利用badrate图进一步调整分箱

# 2.5.1、自定义调整分箱示例

# 2.5.2、绘制每一箱的占比柱状图、及其对应的坏样本率折线图

 # 2.5.3、调整分箱：使得bad_rate整体上呈现单调的趋势

 # 2.6、对分箱后的数据进行WOE转换

# 2.7、特征选择

# 3、模型建立、训练、评估

# 3.1、切分训练集、测试集

# 3.2、模型训练

# 3.3、模型评估：F1、KS、AUC

# 4、模型上线评估，并计算信用分

# 4.1、评估变量的稳定性PSI：比较训练集和测试集

# 4.2、训练集等频分箱，观测每组的区别

# 4.3、评分卡分数变换

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ML之LoR：基于信用卡数据集利用LoR逻辑回归算法实现如何开发通用信用风险评分卡模型之以toad框架全流程讲解
ML之LoR：基于信用卡数据集利用LoR逻辑回归算法实现如何开发通用信用风险评分卡模型之以toad框架全流程讲解代码实现

基于信用卡数据集利用LoR逻辑回归算法实现如何开发通用信用风险评分卡模型之以toad框架全流程讲解

# 1、定义数据集

# 1.1、加载德国信用卡数据集

将由一组属性描述的债务人分类为良好或不良信用风险的信用数据。 https://archive.ics.uci.edu/ml/datasets/Statlog+

status.of.existing.checking.account

duration.in.month

credit.history

purpose

credit.amount

savings.account.and.bonds

present.employment.since

installment.rate.in.percentage.of.disposable.income

personal.status.and.sex

other.debtors.or.guarantors

present.residence.since

property

age.in.years

other.installment.plans

housing

number.of.existing.credits.at.this.bank

job

number.of.people.being.liable.to.provide.maintenance.for

telephone

foreign.worker

creditability

0

... < 0 DM

6

critical account/ other credits existing (not at this bank)

radio/television

1169

unknown/ no savings account

... >= 7 years

4

male : divorced/separated

none

4

real estate

67

none

own

2

skilled employee / official

1

yes, registered under the customers name

yes

good

1

0 <= ... < 200 DM

48

existing credits paid back duly till now

radio/television

5951

... < 100 DM

1 <= ... < 4 years

2

male : divorced/separated

none

2

real estate

22

none

own

1

skilled employee / official

1

none

yes

bad

2

no checking account

12

critical account/ other credits existing (not at this bank)

education

2096

... < 100 DM

4 <= ... < 7 years

2

male : divorced/separated

none

3

real estate

49

none

own

1

unskilled - resident

2

none

yes

good

3

... < 0 DM

42

existing credits paid back duly till now

furniture/equipment

7882

... < 100 DM

4 <= ... < 7 years

2

male : divorced/separated

guarantor

4

building society savings agreement/ life insurance

45

none

for free

1

skilled employee / official

2

none

yes

good

4

... < 0 DM

24

delay in paying off in the past

car (new)

4870

... < 100 DM

1 <= ... < 4 years

3

male : divorced/separated

none

4

unknown / no property

53

none

for free

2

skilled employee / official

2

none

yes

bad

5

no checking account

36

existing credits paid back duly till now

education

9055

unknown/ no savings account

1 <= ... < 4 years

2

male : divorced/separated

none

4

unknown / no property

35

none

for free

1

unskilled - resident

2

yes, registered under the customers name

yes

good

6

no checking account

24

existing credits paid back duly till now

furniture/equipment

2835

500 <= ... < 1000 DM

... >= 7 years

3

male : divorced/separated

none

4

building society savings agreement/ life insurance

53

none

own

1

skilled employee / official

1

none

yes

good

7

0 <= ... < 200 DM

36

existing credits paid back duly till now

car (used)

6948

... < 100 DM

1 <= ... < 4 years

2

male : divorced/separated

none

2

car or other, not in attribute Savings account/bonds

35

none

rent

1

management/ self-employed/ highly qualified employee/ officer

1

yes, registered under the customers name

yes

good

8

no checking account

12

existing credits paid back duly till now

radio/television

3059

... >= 1000 DM

4 <= ... < 7 years

2

male : divorced/separated

none

4

real estate

61

none

own

1

unskilled - resident

1

none

yes

good

9

0 <= ... < 200 DM

30

critical account/ other credits existing (not at this bank)

car (new)

5234

... < 100 DM

unemployed

4

male : divorced/separated

none

2

car or other, not in attribute Savings account/bonds

28

none

own

2

management/ self-employed/ highly qualified employee/ officer

1

none

yes

bad

10

0 <= ... < 200 DM

12

existing credits paid back duly till now

car (new)

1295

... < 100 DM

... < 1 year

3

male : divorced/separated

none

1

car or other, not in attribute Savings account/bonds

25

none

rent

1

skilled employee / official

1

none

yes

bad

11

... < 0 DM

48

existing credits paid back duly till now

business

4308

... < 100 DM

... < 1 year

3

male : divorced/separated

none

4

building society savings agreement/ life insurance

24

none

rent

1

skilled employee / official

1

none

yes

bad

12

0 <= ... < 200 DM

12

existing credits paid back duly till now

radio/television

1567

... < 100 DM

1 <= ... < 4 years

1

male : divorced/separated

none

1

car or other, not in attribute Savings account/bonds

22

none

own

1

skilled employee / official

1

yes, registered under the customers name

yes

good

13

... < 0 DM

24

critical account/ other credits existing (not at this bank)

car (new)

1199

... < 100 DM

... >= 7 years

4

male : divorced/separated

none

4

car or other, not in attribute Savings account/bonds

60

none

own

2

unskilled - resident

1

none

yes

bad

14

... < 0 DM

15

existing credits paid back duly till now

car (new)

1403

... < 100 DM

1 <= ... < 4 years

2

male : divorced/separated

none

4

car or other, not in attribute Savings account/bonds

28

none

rent

1

skilled employee / official

1

none

yes

good

15

... < 0 DM

24

existing credits paid back duly till now

radio/television

1282

100 <= ... < 500 DM

1 <= ... < 4 years

4

male : divorced/separated

none

2

car or other, not in attribute Savings account/bonds

32

none

own

1

unskilled - resident

1

none

yes

bad

16

no checking account

24

critical account/ other credits existing (not at this bank)

radio/television

2424

unknown/ no savings account

... >= 7 years

4

male : divorced/separated

none

4

building society savings agreement/ life insurance

53

none

own

2

skilled employee / official

1

none

yes

good

17

... < 0 DM

30

no credits taken/ all credits paid back duly

business

8072

unknown/ no savings account

... < 1 year

2

male : divorced/separated

none

3

car or other, not in attribute Savings account/bonds

25

bank

own

3

skilled employee / official

1

none

yes

good

18

0 <= ... < 200 DM

24

existing credits paid back duly till now

car (used)

12579

... < 100 DM

... >= 7 years

4

male : divorced/separated

none

2

unknown / no property

44

none

for free

1

management/ self-employed/ highly qualified employee/ officer

1

yes, registered under the customers name

yes

bad

19

no checking account

24

existing credits paid back duly till now

radio/television

3430

500 <= ... < 1000 DM

... >= 7 years

3

male : divorced/separated

none

2

car or other, not in attribute Savings account/bonds

31

none

own

1

skilled employee / official

2

yes, registered under the customers name

yes

good

#1.2、对各个变量进行EDA分析

# 数值型变量：数据类型、缺失率、唯一值、均值、标准差、分位数等，
# 分类型变量：数据类型、缺失率、唯一值、top1(占比第一的数据类)等。

	type	size	missing	unique	mean_or_top1	std_or_top2	min_or_top3	1%_or_top4	10%_or_top5	50%_or_bottom5	75%_or_bottom4	90%_or_bottom3	99%_or_bottom2	max_or_bottom1
status.of.existing.checking.account	category	1000	0.00%	4	no checking account:39.40%	... < 0 DM:27.40%	0 <= ... < 200 DM:26.90%	... >= 200 DM / salary assignments for at least 1 year:6.30%			no checking account:39.40%	... < 0 DM:27.40%	0 <= ... < 200 DM:26.90%	... >= 200 DM / salary assignments for at least 1 year:6.30%
duration.in.month	int64	1000	0.00%	33	20.903	12.05881445	4	6	9	18	24	36	60	72
credit.history	category	1000	0.00%	5	existing credits paid back duly till now:53.00%	critical account/ other credits existing (not at this bank):29.30%	delay in paying off in the past:8.80%	all credits at this bank paid back duly:4.90%	no credits taken/ all credits paid back duly:4.00%	existing credits paid back duly till now:53.00%	critical account/ other credits existing (not at this bank):29.30%	delay in paying off in the past:8.80%	all credits at this bank paid back duly:4.90%	no credits taken/ all credits paid back duly:4.00%
purpose	object	1000	0.00%	10	radio/television:28.00%	car (new):23.40%	furniture/equipment:18.10%	car (used):10.30%	business:9.70%	education:5.00%	repairs:2.20%	domestic appliances:1.20%	others:1.20%	retraining:0.90%
credit.amount	int64	1000	0.00%	921	3271.258	2822.736876	250	425.83	932	2319.5	3972.25	7179.4	14180.39	18424
savings.account.and.bonds	category	1000	0.00%	5	... < 100 DM:60.30%	unknown/ no savings account:18.30%	100 <= ... < 500 DM:10.30%	500 <= ... < 1000 DM:6.30%	... >= 1000 DM:4.80%	... < 100 DM:60.30%	unknown/ no savings account:18.30%	100 <= ... < 500 DM:10.30%	500 <= ... < 1000 DM:6.30%	... >= 1000 DM:4.80%
present.employment.since	category	1000	0.00%	5	1 <= ... < 4 years:33.90%	... >= 7 years:25.30%	4 <= ... < 7 years:17.40%	... < 1 year:17.20%	unemployed:6.20%	1 <= ... < 4 years:33.90%	... >= 7 years:25.30%	4 <= ... < 7 years:17.40%	... < 1 year:17.20%	unemployed:6.20%
installment.rate.in.percentage.of.disposable.income	int64	1000	0.00%	4	2.973	1.118714674	1	1	1	3	4	4	4	4
personal.status.and.sex	category	1000	0.00%	4	male : single:54.80%	female : divorced/separated/married:31.00%	male : married/widowed:9.20%	male : divorced/separated:5.00%	female : single:0.00%	male : single:54.80%	female : divorced/separated/married:31.00%	male : married/widowed:9.20%	male : divorced/separated:5.00%	female : single:0.00%
other.debtors.or.guarantors	category	1000	0.00%	3	none:90.70%	guarantor:5.20%	co-applicant:4.10%					none:90.70%	guarantor:5.20%	co-applicant:4.10%
present.residence.since	int64	1000	0.00%	4	2.845	1.103717896	1	1	1	3	4	4	4	4
property	category	1000	0.00%	4	car or other, not in attribute Savings account/bonds:33.20%	real estate:28.20%	building society savings agreement/ life insurance:23.20%	unknown / no property:15.40%			car or other, not in attribute Savings account/bonds:33.20%	real estate:28.20%	building society savings agreement/ life insurance:23.20%	unknown / no property:15.40%
age.in.years	int64	1000	0.00%	53	35.546	11.37546857	19	20	23	33	42	52	67.01	75
other.installment.plans	category	1000	0.00%	3	none:81.40%	bank:13.90%	stores:4.70%					none:81.40%	bank:13.90%	stores:4.70%
housing	category	1000	0.00%	3	own:71.30%	rent:17.90%	for free:10.80%					own:71.30%	rent:17.90%	for free:10.80%
number.of.existing.credits.at.this.bank	int64	1000	0.00%	4	1.407	0.577654468	1	1	1	1	2	2	3	4
job	category	1000	0.00%	4	skilled employee / official:63.00%	unskilled - resident:20.00%	management/ self-employed/ highly qualified employee/ officer:14.80%	unemployed/ unskilled - non-resident:2.20%			skilled employee / official:63.00%	unskilled - resident:20.00%	management/ self-employed/ highly qualified employee/ officer:14.80%	unemployed/ unskilled - non-resident:2.20%
number.of.people.being.liable.to.provide.maintenance.for	int64	1000	0.00%	2	1.155	0.362085772	1	1	1	1	1	2	2	2
telephone	category	1000	0.00%	2	none:59.60%	yes, registered under the customers name:40.40%							none:59.60%	yes, registered under the customers name:40.40%
foreign.worker	category	1000	0.00%	2	yes:96.30%	no:3.70%							yes:96.30%	no:3.70%
creditability	object	1000	0.00%	2	good:70.00%	bad:30.00%							good:70.00%	bad:30.00%

# 1.3、输出连续型变量的mean、std、min、3种分位数、max

	duration.in.month	credit.amount	installment.rate.in.percentage.of.disposable.income	present.residence.since	age.in.years	number.of.existing.credits.at.this.bank	number.of.people.being.liable.to.provide.maintenance.for
mean	20.903	3271.258	2.973	2.845	35.546	1.407	1.155
std	12.05881445	2822.736876	1.118714674	1.103717896	11.37546857	0.577654468	0.362085772
min	4	250	1	1	19	1	1
25%	12	1365.5	2	2	27	1	1
50%	18	2319.5	3	3	33	1	1
75%	24	3972.25	4	4	42	2	1
max	72	18424	4	4	75	4	2

# 2、数据预处理

# 2.1、对类别型目标变量映射成数值型变量

# 2.2、分析每个特征的iv、基尼系数gini、熵entropy、unique等

	iv	gini	entropy	unique
creditability	12.22649152	0	0	2
status.of.existing.checking.account	0.666011503	0.368037204	0.545196341	4
duration.in.month	0.354783574	0.406755043	0.609659161	33
credit.amount	0.351454966	0.408679834	0.610864302	921
credit.history	0.293233547	0.394089613	0.580630747	5
age.in.years	0.21119662	0.41433928	0.610863206	53
savings.account.and.bonds	0.196009557	0.40483845	0.591376694	5
purpose	0.169195066	0.405990292	0.593609415	10
property	0.112638262	0.410037788	0.599091068	4
present.employment.since	0.086433631	0.412285325	0.601782464	5
housing	0.083293434	0.412356067	0.602024467	3
other.installment.plans	0.057614542	0.414607541	0.604712572	3
foreign.worker	0.043877412	0.417170441	0.606828112	2
other.debtors.or.guarantors	0.032019322	0.417208946	0.607539261	3
installment.rate.in.percentage.of.disposable.income	0.02632209	0.417699747	0.60811103	4
number.of.existing.credits.at.this.bank	0.013266524	0.418878097	0.609493027	4
personal.status.and.sex	0.008839919	0.419238171	0.609944287	4
job	0.008762766	0.419208234	0.609937317	4
telephone	0.006377605	0.419441491	0.610196344	2
present.residence.since	0.003588773	0.419685295	0.610488269	4
number.of.people.being.liable.to.provide.maintenance.for	4.34E-05	0.419996182	0.61085975	2

# 2.3、筛选特征：分别基于IV、empty、corr指标

drop_cols: 
 {'empty': array([], dtype=float64), 'iv': array(['personal.status.and.sex', 'present.residence.since',
       'number.of.existing.credits.at.this.bank', 'job',
       'number.of.people.being.liable.to.provide.maintenance.for',
       'telephone'], dtype=object), 'corr': array([], dtype=object)}

# 2.4、分箱处理

对数值型变量和分类型变量进行分箱，分箱方法支持卡方chi、决策树、百分位、等频、等距分箱

data_df_s2bins_dict: 
 {'status.of.existing.checking.account': [['no checking account'], ['... >= 200 DM / salary assignments for at least 1 year'], ['0 <= ... < 200 DM'], ['... < 0 DM']], 'duration.in.month': [9, 12, 13, 16, 36, 45], 'credit.history': [['critical account/ other credits existing (not at this bank)'], ['delay in paying off in the past', 'existing credits paid back duly till now'], ['all credits at this bank paid back duly', 'no credits taken/ all credits paid back duly']], 'purpose': [['retraining', 'car (used)'], ['radio/television'], ['furniture/equipment'], ['domestic appliances', 'business', 'repairs'], ['car (new)'], ['others', 'education']], 'credit.amount': [3556], 'savings.account.and.bonds': [['... >= 1000 DM', '500 <= ... < 1000 DM', 'unknown/ no savings account'], ['100 <= ... < 500 DM'], ['... < 100 DM']], 'present.employment.since': [['4 <= ... < 7 years'], ['... >= 7 years'], ['1 <= ... < 4 years'], ['unemployed'], ['... < 1 year']], 'installment.rate.in.percentage.of.disposable.income': [2, 3, 4], 'other.debtors.or.guarantors': [['guarantor', 'none', 'co-applicant']], 'property': [['real estate'], ['building society savings agreement/ life insurance'], ['car or other, not in attribute Savings account/bonds'], ['unknown / no property']], 'age.in.years': [26, 35, 37, 49], 'other.installment.plans': [['none'], ['stores', 'bank']], 'housing': [['own'], ['rent'], ['for free']], 'foreign.worker': [['no', 'yes']], 'creditability': [['good'], ['bad']]}

# 2.5、利用badrate图进一步调整分箱

# 2.5.1、自定义调整分箱示例

# 2.5.2、绘制每一箱的占比柱状图、及其对应的坏样本率折线图

 

 # 2.5.3、调整分箱：使得bad_rate整体上呈现单调的趋势

 # 2.6、对分箱后的数据进行WOE转换

	status.of.existing.checking.account	duration.in.month	credit.history	purpose	credit.amount	savings.account.and.bonds	present.employment.since	installment.rate.in.percentage.of.disposable.income	other.debtors.or.guarantors	property	age.in.years	other.installment.plans	housing	foreign.worker	creditability	creditability_map
0	0.818098706	-1.280933845	-0.733740578	-0.410062817	-0.153492135	-0.762140052	-0.235566071	0.157300289	0	-0.461034959	-0.194156014	-0.121178625	-0.194156014	0	-5.703782475	0
1	0.401391783	1.134979933	0.087868755	-0.410062817	0.31563815	0.271357844	0.032103245	-0.155466469	0	-0.461034959	0.48083491	-0.121178625	-0.194156014	0	6.551080335	1
2	-1.176263223	-0.128416292	-0.733740578	0.587786665	-0.153492135	0.271357844	-0.394415272	-0.155466469	0	-0.461034959	-0.266352306	-0.121178625	-0.194156014	0	-5.703782475	0
3	0.818098706	0.524524468	0.087868755	0.095556516	0.31563815	0.271357844	-0.394415272	-0.155466469	0	0.028573372	-0.266352306	-0.121178625	0.472604411	0	-5.703782475	0
4	0.818098706	0.108688306	0.087868755	0.359200488	0.31563815	0.271357844	0.032103245	-0.064538521	0	0.586082361	-0.266352306	-0.121178625	0.472604411	0	6.551080335	1
5	-1.176263223	0.524524468	0.087868755	0.587786665	0.31563815	-0.762140052	0.032103245	-0.155466469	0	0.586082361	-0.044353168	-0.121178625	0.472604411	0	-5.703782475	0
6	-1.176263223	0.108688306	0.087868755	0.095556516	-0.153492135	-0.762140052	-0.235566071	-0.064538521	0	0.028573372	-0.266352306	-0.121178625	-0.194156014	0	-5.703782475	0
7	0.401391783	0.524524468	0.087868755	-0.805625164	0.31563815	0.271357844	0.032103245	-0.155466469	0	0.034191365	-0.044353168	-0.121178625	0.40444522	0	-5.703782475	0
8	-1.176263223	-0.128416292	0.087868755	-0.410062817	-0.153492135	-0.762140052	-0.394415272	-0.155466469	0	-0.461034959	-0.266352306	-0.121178625	-0.194156014	0	-5.703782475	0
9	0.401391783	0.108688306	-0.733740578	0.359200488	0.31563815	0.271357844	0.31923043	0.157300289	0	0.034191365	-0.044353168	-0.121178625	-0.194156014	0	6.551080335	1
10	0.401391783	-0.128416292	0.087868755	0.359200488	-0.153492135	0.271357844	0.470820289	-0.064538521	0	0.034191365	-0.044353168	-0.121178625	0.40444522	0	6.551080335	1
11	0.818098706	1.134979933	0.087868755	0.233288	0.31563815	0.271357844	0.470820289	-0.064538521	0	0.028573372	0.48083491	-0.121178625	0.40444522	0	6.551080335	1
12	0.401391783	-0.128416292	0.087868755	-0.410062817	-0.153492135	0.271357844	0.032103245	-0.251314428	0	0.034191365	0.48083491	-0.121178625	-0.194156014	0	-5.703782475	0
13	0.818098706	0.108688306	-0.733740578	0.359200488	-0.153492135	0.271357844	-0.235566071	0.157300289	0	0.034191365	-0.266352306	-0.121178625	-0.194156014	0	6.551080335	1
14	0.818098706	-0.665290226	0.087868755	0.359200488	-0.153492135	0.271357844	0.032103245	-0.155466469	0	0.034191365	-0.044353168	-0.121178625	0.40444522	0	-5.703782475	0
15	0.818098706	0.108688306	0.087868755	-0.410062817	-0.153492135	0.13955188	0.032103245	0.157300289	0	0.034191365	-0.044353168	-0.121178625	-0.194156014	0	6.551080335	1
16	-1.176263223	0.108688306	-0.733740578	-0.410062817	-0.153492135	-0.762140052	-0.235566071	0.157300289	0	0.028573372	-0.266352306	-0.121178625	-0.194156014	0	-5.703782475	0
17	0.818098706	0.108688306	1.234070835	0.233288	0.31563815	-0.762140052	0.470820289	-0.155466469	0	0.034191365	-0.044353168	0.477550835	-0.194156014	0	-5.703782475	0
18	0.401391783	0.108688306	0.087868755	-0.805625164	0.31563815	0.271357844	-0.235566071	0.157300289	0	0.586082361	-0.044353168	-0.121178625	0.472604411	0	6.551080335	1
19	-1.176263223	0.108688306	0.087868755	-0.410062817	-0.153492135	-0.762140052	-0.235566071	-0.064538521	0	0.034191365	-0.044353168	-0.121178625	-0.194156014	0	-5.703782475	0

# 2.7、特征选择

# 通过向前、向后、双向选择来进行特征选择，使用aic、bic、ks、auc 作为选择标准

final_data: 
 (1000, 3)
final_data: 
 Index(['status.of.existing.checking.account', 'creditability',
       'creditability_map'],
      dtype='object')

# 3、模型建立、训练、评估

# 3.1、切分训练集、测试集

# 3.2、模型训练

# 3.3、模型评估：F1、KS、AUC

# 4、模型上线评估，并计算信用分

# 4.1、评估变量的稳定性PSI：比较训练集和测试集

cal PSI 0.012897491574571578

# 4.2、训练集等频分箱，观测每组的区别

	min	max	bads	goods	total	bad_rate	good_rate	odds	bad_prop	good_prop	total_prop	cum_bad_rate	cum_bad_rate_rev	cum_bads_prop	cum_bads_prop_rev	cum_goods_prop	cum_goods_prop_rev	cum_total_prop	cum_total_prop_rev	ks	lift
0	0.000194976	0.000204106	0	292	292	0	1	0	0	0.5583174	0.389333333	0	0.302666667	0	1	0.5583174	1	0.389333333	1	0.5583174	1
1	0.000214122	0.000214122	0	125	125	0	1	0	0	0.239005736	0.166666667	0	0.495633188	0	1	0.797323136	0.4416826	0.556	0.610666667	0.797323136	1.637554585
2	0.000219486	0.000219486	0	106	106	0	1	0	0	0.202676864	0.141333333	0	0.681681682	0	1	1	0.202676864	0.697333333	0.444	1	2.252252252
3	0.999484936	0.99950797	48	0	48	1	0	inf	0.211453744	0	0.064	0.084063047	1	0.211453744	1	1	0	0.761333333	0.302666667	0.788546256	3.303964758
4	0.99953098	0.99953098	78	0	78	1	0	inf	0.343612335	0	0.104	0.194144838	1	0.555066079	0.788546256	1	0	0.865333333	0.238666667	0.444933921	3.303964758
5	0.999542439	0.999542439	101	0	101	1	0	inf	0.444933921	0	0.134666667	0.302666667	1	1	0.444933921	1	0	1	0.134666667	0	3.303964758

# 4.3、评分卡分数变换

	name	value	score
0	status.of.existing.checking.account	no checking account	261.94
1	status.of.existing.checking.account	... >= 200 DM / salary assignments for at least 1 year	258.87
2	status.of.existing.checking.account	0 <= ... < 200 DM	255.66
3	status.of.existing.checking.account	... < 0 DM	254.01
4	creditability	good	744.2
5	creditability	bad	-302.01