compactCreditScorecard Объектный рабочий процессЭтот пример показывает рабочий процесс для создания compactCreditScorecard объект от creditscorecard объект.
creditscorecard объектСоздать compactCreditScorecard объект, необходимо сначала создать creditscorecard объект. Создайте creditscorecard объект с CreditCardData.mat файл и набор аргумент пары "имя-значение" 'BinMissingData' к true потому что dataMissing набор данных содержит недостающие данные.
load CreditCardData.mat sc = creditscorecard(dataMissing,'IDVar','CustID','BinMissingData',true); sc = autobinning(sc); sc = modifybins(sc,'CustAge','MinValue',0); sc = modifybins(sc,'CustIncome','MinValue',0);
creditscorecard объектИспользуйте fitmodel подбирать модель логистической регрессии использование данных о Весе доказательства (WOE).
[sc, mdl] = fitmodel(sc);
1. Adding CustIncome, Deviance = 1490.8527, Chi2Stat = 32.588614, PValue = 1.1387992e-08
2. Adding TmWBank, Deviance = 1467.1415, Chi2Stat = 23.711203, PValue = 1.1192909e-06
3. Adding AMBalance, Deviance = 1455.5715, Chi2Stat = 11.569967, PValue = 0.00067025601
4. Adding EmpStatus, Deviance = 1447.3451, Chi2Stat = 8.2264038, PValue = 0.0041285257
5. Adding CustAge, Deviance = 1442.8477, Chi2Stat = 4.4974731, PValue = 0.033944979
6. Adding ResStatus, Deviance = 1438.9783, Chi2Stat = 3.86941, PValue = 0.049173805
7. Adding OtherCC, Deviance = 1434.9751, Chi2Stat = 4.0031966, PValue = 0.045414057
Generalized linear regression model:
status ~ [Linear formula with 8 terms in 7 predictors]
Distribution = Binomial
Estimated Coefficients:
Estimate SE tStat pValue
________ ________ ______ __________
(Intercept) 0.70229 0.063959 10.98 4.7498e-28
CustAge 0.57421 0.25708 2.2335 0.025513
ResStatus 1.3629 0.66952 2.0356 0.04179
EmpStatus 0.88373 0.2929 3.0172 0.002551
CustIncome 0.73535 0.2159 3.406 0.00065929
TmWBank 1.1065 0.23267 4.7556 1.9783e-06
OtherCC 1.0648 0.52826 2.0156 0.043841
AMBalance 1.0446 0.32197 3.2443 0.0011775
1200 observations, 1192 error degrees of freedom
Dispersion: 1
Chi^2-statistic vs. constant model: 88.5, p-value = 2.55e-16
creditscorecard объектСоздайте новый набор данных, который используется для выигрыша на основе ранее созданного creditscorecard объект.
tdata = data(1:10, mdl.PredictorNames); tdata.CustAge(2) = NaN; tdata.CustAge(5) = -5; tdata.ResStatus(1) = '<undefined>'; tdata.ResStatus(3) = 'Landlord'; tdata.EmpStatus(3) = '<undefined>'; tdata.CustIncome(4) = NaN; tdata.EmpStatus(7) = 'Freelancer'; tdata.CustIncome(8) = -1; tdata.CustIncome(4) = NaN; disp(tdata);
CustAge ResStatus EmpStatus CustIncome TmWBank OtherCC AMBalance
_______ ___________ ___________ __________ _______ _______ _________
53 <undefined> Unknown 50000 55 Yes 1055.9
NaN Home Owner Employed 52000 25 Yes 1161.6
47 Landlord <undefined> 37000 61 No 877.23
50 Home Owner Employed NaN 20 Yes 157.37
-5 Home Owner Employed 53000 14 Yes 561.84
65 Home Owner Employed 48000 59 Yes 968.18
34 Home Owner Freelancer 32000 26 Yes 717.82
50 Other Employed -1 33 No 3041.2
50 Tenant Unknown 52000 25 Yes 115.56
49 Home Owner Unknown 53000 23 Yes 718.5
Используйте displaypoints отобразить точки на предиктор. Используйте score вычислить кредитные рейтинги с помощью новых данных (tdata). Затем используйте probdefault с новыми данными (tdata) вычислить вероятность значения по умолчанию. При использовании formatpoints, 'Missing' аргумент пары "имя-значение" установлен в 'minpoints' потому что tdata содержит недостающие данные.
PointsInfo = displaypoints(sc)
PointsInfo=38×3 table
Predictors Bin Points
_____________ ______________ _________
{'CustAge' } {'[0,33)' } -0.14173
{'CustAge' } {'[33,37)' } -0.11095
{'CustAge' } {'[37,40)' } -0.059244
{'CustAge' } {'[40,46)' } 0.074167
{'CustAge' } {'[46,48)' } 0.1889
{'CustAge' } {'[48,51)' } 0.20204
{'CustAge' } {'[51,58)' } 0.22935
{'CustAge' } {'[58,Inf]' } 0.45019
{'CustAge' } {'<missing>' } 0.0096749
{'ResStatus'} {'Tenant' } -0.029778
{'ResStatus'} {'Home Owner'} 0.12425
{'ResStatus'} {'Other' } 0.36796
{'ResStatus'} {'<missing>' } 0.1364
{'EmpStatus'} {'Unknown' } -0.075948
{'EmpStatus'} {'Employed' } 0.31401
{'EmpStatus'} {'<missing>' } NaN
⋮
[Scores, Points] = score(sc, tdata)
Scores = 10×1
1.2784
1.0071
NaN
NaN
0.9960
1.8771
NaN
NaN
1.0283
0.8095
Points=10×7 table
CustAge ResStatus EmpStatus CustIncome TmWBank OtherCC AMBalance
_________ _________ _________ __________ _________ ________ _________
0.22935 0.1364 -0.075948 0.45309 0.3958 0.15715 -0.017438
0.0096749 0.12425 0.31401 0.45309 -0.033652 0.15715 -0.017438
0.1889 0.1364 NaN 0.080697 0.3958 -0.18537 -0.017438
0.20204 0.12425 0.31401 NaN -0.044701 0.15715 0.35539
0.0096749 0.12425 0.31401 0.45309 -0.044701 0.15715 -0.017438
0.45019 0.12425 0.31401 0.45309 0.3958 0.15715 -0.017438
-0.11095 0.12425 NaN -0.11452 -0.033652 0.15715 -0.017438
0.20204 0.36796 0.31401 NaN -0.033652 -0.18537 -0.21195
0.20204 -0.029778 -0.075948 0.45309 -0.033652 0.15715 0.35539
0.20204 0.12425 -0.075948 0.45309 -0.033652 0.15715 -0.017438
pd = probdefault(sc, tdata)
pd = 10×1
0.2178
0.2676
NaN
NaN
0.2697
0.1327
NaN
NaN
0.2634
0.3080
sc = formatpoints(sc,'BasePoints',true,'Missing','minpoints','Round','finalscore','PointsOddsAndPDO',[500, 2, 50]); PointsInfo1 = displaypoints(sc)
PointsInfo1=39×3 table
Predictors Bin Points
______________ ______________ _______
{'BasePoints'} {'BasePoints'} 500.66
{'CustAge' } {'[0,33)' } -17.461
{'CustAge' } {'[33,37)' } -15.24
{'CustAge' } {'[37,40)' } -11.511
{'CustAge' } {'[40,46)' } -1.8871
{'CustAge' } {'[46,48)' } 6.3888
{'CustAge' } {'[48,51)' } 7.3367
{'CustAge' } {'[51,58)' } 9.3068
{'CustAge' } {'[58,Inf]' } 25.238
{'CustAge' } {'<missing>' } -6.5392
{'ResStatus' } {'Tenant' } -9.3852
{'ResStatus' } {'Home Owner'} 1.7253
{'ResStatus' } {'Other' } 19.305
{'ResStatus' } {'<missing>' } 2.6022
{'EmpStatus' } {'Unknown' } -12.716
{'EmpStatus' } {'Employed' } 15.414
⋮
[Scores1, Points1] = score(sc, tdata)
Scores1 = 10×1
542
523
488
495
522
585
445
448
524
508
Points1=10×8 table
BasePoints CustAge ResStatus EmpStatus CustIncome TmWBank OtherCC AMBalance
__________ _______ _________ _________ __________ _______ _______ _________
500.66 9.3068 2.6022 -12.716 25.446 21.314 4.0988 -8.495
500.66 -6.5392 1.7253 15.414 25.446 -9.6646 4.0988 -8.495
500.66 6.3888 2.6022 -12.716 -1.4161 21.314 -20.609 -8.495
500.66 7.3367 1.7253 15.414 -42.148 -10.462 4.0988 18.399
500.66 -6.5392 1.7253 15.414 25.446 -10.462 4.0988 -8.495
500.66 25.238 1.7253 15.414 25.446 21.314 4.0988 -8.495
500.66 -15.24 1.7253 -12.716 -15.498 -9.6646 4.0988 -8.495
500.66 7.3367 19.305 15.414 -42.148 -9.6646 -20.609 -22.526
500.66 7.3367 -9.3852 -12.716 25.446 -9.6646 4.0988 18.399
500.66 7.3367 1.7253 -12.716 25.446 -9.6646 4.0988 -8.495
pd1 = probdefault(sc, tdata)
pd1 = 10×1
0.2178
0.2676
0.3721
0.3488
0.2697
0.1327
0.5178
0.5077
0.2634
0.3080
compactCreditScorecard объект от creditscorecard объектСоздайте compactCreditScorecard объект с помощью creditscorecard возразите как вход. В качестве альтернативы можно создать compactCreditScorecard объект с помощью compact функция в Financial Toolbox™.
csc = compactCreditScorecard(sc)
csc =
compactCreditScorecard with properties:
Description: ''
GoodLabel: 0
ResponseVar: 'status'
WeightsVar: ''
NumericPredictors: {'CustAge' 'CustIncome' 'TmWBank' 'AMBalance'}
CategoricalPredictors: {'ResStatus' 'EmpStatus' 'OtherCC'}
PredictorVars: {1x7 cell}
compactCreditScorecard объектМожно анализировать объект compactCreditScorecard с displaypoints, score, и probdefault от Risk Management Toolbox™.
PointsInfo2 = displaypoints(csc)
PointsInfo2=39×3 table
Predictors Bin Points
______________ ______________ _______
{'BasePoints'} {'BasePoints'} 500.66
{'CustAge' } {'[0,33)' } -17.461
{'CustAge' } {'[33,37)' } -15.24
{'CustAge' } {'[37,40)' } -11.511
{'CustAge' } {'[40,46)' } -1.8871
{'CustAge' } {'[46,48)' } 6.3888
{'CustAge' } {'[48,51)' } 7.3367
{'CustAge' } {'[51,58)' } 9.3068
{'CustAge' } {'[58,Inf]' } 25.238
{'CustAge' } {'<missing>' } -6.5392
{'ResStatus' } {'Tenant' } -9.3852
{'ResStatus' } {'Home Owner'} 1.7253
{'ResStatus' } {'Other' } 19.305
{'ResStatus' } {'<missing>' } 2.6022
{'EmpStatus' } {'Unknown' } -12.716
{'EmpStatus' } {'Employed' } 15.414
⋮
[Scores2, Points2] = score(csc, tdata)
Scores2 = 10×1
542
523
488
495
522
585
445
448
524
508
Points2=10×8 table
BasePoints CustAge ResStatus EmpStatus CustIncome TmWBank OtherCC AMBalance
__________ _______ _________ _________ __________ _______ _______ _________
500.66 9.3068 2.6022 -12.716 25.446 21.314 4.0988 -8.495
500.66 -6.5392 1.7253 15.414 25.446 -9.6646 4.0988 -8.495
500.66 6.3888 2.6022 -12.716 -1.4161 21.314 -20.609 -8.495
500.66 7.3367 1.7253 15.414 -42.148 -10.462 4.0988 18.399
500.66 -6.5392 1.7253 15.414 25.446 -10.462 4.0988 -8.495
500.66 25.238 1.7253 15.414 25.446 21.314 4.0988 -8.495
500.66 -15.24 1.7253 -12.716 -15.498 -9.6646 4.0988 -8.495
500.66 7.3367 19.305 15.414 -42.148 -9.6646 -20.609 -22.526
500.66 7.3367 -9.3852 -12.716 25.446 -9.6646 4.0988 18.399
500.66 7.3367 1.7253 -12.716 25.446 -9.6646 4.0988 -8.495
pd2 = probdefault(csc, tdata)
pd2 = 10×1
0.2178
0.2676
0.3721
0.3488
0.2697
0.1327
0.5178
0.5077
0.2634
0.3080
Сравните размер creditscorecard и compactCreditScorecard объекты.
whos('dataMissing','sc','csc')
Name Size Bytes Class Attributes csc 1x1 39609 compactCreditScorecard dataMissing 1200x11 84604 table sc 1x1 166585 creditscorecard
Размер compactCreditScorecard объект легок по сравнению с creditscorecard объект. Однако compactCreditScorecard объект не может быть непосредственно изменен. Если необходимо изменить compactCreditScorecard объект, необходимо изменить стартовый creditscorecard объект, и затем повторно преобразует тот объект создать compactCreditScorecard возразите снова.
autobinning | bindata | bininfo | creditscorecard | displaypoints | fitmodel | formatpoints | modifybins | modifypredictor | plotbins | predictorinfo | probdefault | score | screenpredictors | setmodel | validatemodel