В этом примере показано, как сравнить новый Logistic модель для пожизненного PD против модели "чемпиона".
Загрузите данные о портфеле, которые включают ссуду и макро-информацию.
load RetailCreditPanelData.mat
data = join(data,dataMacro);
disp(head(data)) ID ScoreGroup YOB Default Year GDP Market
__ __________ ___ _______ ____ _____ ______
1 Low Risk 1 0 1997 2.72 7.61
1 Low Risk 2 0 1998 3.57 26.24
1 Low Risk 3 0 1999 2.86 18.1
1 Low Risk 4 0 2000 2.43 3.19
1 Low Risk 5 0 2001 1.26 -10.51
1 Low Risk 6 0 2002 -0.59 -22.95
1 Low Risk 7 0 2003 0.63 2.78
1 Low Risk 8 0 2004 1.85 9.48
nIDs = max(data.ID); uniqueIDs = unique(data.ID); rng('default'); % for reproducibility c = cvpartition(nIDs,'HoldOut',0.4); TrainIDInd = training(c); TestIDInd = test(c); TrainDataInd = ismember(data.ID,uniqueIDs(TrainIDInd)); TestDataInd = ismember(data.ID,uniqueIDs(TestIDInd));
В данном примере соответствуйте, новая модель с помощью только выигрывают информацию о группе, но никакую информацию о возрасте. Во-первых, можно подтвердить эту модель автономным способом. Для получения дополнительной информации смотрите Основную Пожизненную Проверку допустимости модели PD.
Информация о возрасте важна в этом наборе данных. Новая модель не выполняет, а также модель чемпиона (который включает возраст, группу счета и макро-Вар).
Соответствуйте новому Logistic модель с помощью fitLifetimePDModel.
ModelType = "logistic"; pdModel = fitLifetimePDModel(data(TrainDataInd,:),ModelType,... 'ModelID','LogisticNoAge',... 'IDVar','ID',... 'LoanVars','ScoreGroup',... 'MacroVars',{'GDP','Market'},... 'ResponseVar','Default'); disp(pdModel)
Logistic with properties:
ModelID: "LogisticNoAge"
Description: ""
Model: [1x1 classreg.regr.CompactGeneralizedLinearModel]
IDVar: "ID"
AgeVar: ""
LoanVars: "ScoreGroup"
MacroVars: ["GDP" "Market"]
ResponseVar: "Default"
Сравнить новый Logistic модель к модели чемпиона, вам нужен доступ к предсказаниям модели чемпиона. Сила чемпиона модели даже имеет различные предикторы, таким образом, отображение между используемыми данными и точными входными параметрами чемпиона сила модели требует промежуточного шага предварительной обработки. Этот пример принимает, что у вас есть инструмент черного ящика, чтобы получить предсказания из модели чемпиона.
Сравните производительность модели для обеих моделей с помощью modelDiscrimination.
DataSetChoice ="Testing"; if DataSetChoice =="Training" Ind = TrainDataInd; else Ind = TestDataInd; end ChampionPD = getChampionModelPDs (данные (Ind, :)); [DiscMeasure, DiscData] = modelDiscrimination (pdModel, данные (Ind, :),'DataID', DataSetChoice,... 'ReferencePD', ChampionPD,'ReferenceID',"Champion"); disp (DiscMeasure)
AUROC
_______
LogisticNoAge, Testing 0.66503
Champion, Testing 0.70018
disp(head(DiscData))
ModelID X Y T
_______________ ________ ________ ________
"LogisticNoAge" 0 0 0.02287
"LogisticNoAge" 0.04673 0.090978 0.02287
"LogisticNoAge" 0.064656 0.14922 0.022711
"LogisticNoAge" 0.10982 0.22764 0.020553
"LogisticNoAge" 0.14421 0.311 0.018483
"LogisticNoAge" 0.19237 0.41454 0.01722
"LogisticNoAge" 0.23558 0.43738 0.014125
"LogisticNoAge" 0.27979 0.52037 0.012812
disp(tail(DiscData))
ModelID X Y T
__________ _______ _______ __________
"Champion" 0.88743 0.98021 0.0032242
"Champion" 0.90293 0.98477 0.0025583
"Champion" 0.91884 0.98896 0.0023801
"Champion" 0.93303 0.99239 0.0018756
"Champion" 0.94995 0.99391 0.0017711
"Champion" 0.96705 0.99695 0.0016436
"Champion" 0.98295 0.99886 0.0012847
"Champion" 1 1 0.00086887
Используйте modelDiscriminationPlot построить ROC.
modelDiscriminationPlot(pdModel,data(Ind,:),'DataID',DataSetChoice,... 'ReferencePD',ChampionPD,'ReferenceID',"Champion");
[DiscMeasure,DiscData] = modelDiscrimination(pdModel,data(Ind,:),'SegmentBy','YOB','DataID',DataSetChoice,... 'ReferencePD',ChampionPD,'ReferenceID',"Champion"); disp(DiscMeasure)
AUROC
_______
LogisticNoAge, YOB=1, Testing 0.64879
Champion, YOB=1, Testing 0.64972
LogisticNoAge, YOB=2, Testing 0.65699
Champion, YOB=2, Testing 0.66496
LogisticNoAge, YOB=3, Testing 0.63508
Champion, YOB=3, Testing 0.64774
LogisticNoAge, YOB=4, Testing 0.62656
Champion, YOB=4, Testing 0.66204
LogisticNoAge, YOB=5, Testing 0.6205
Champion, YOB=5, Testing 0.65439
LogisticNoAge, YOB=6, Testing 0.61739
Champion, YOB=6, Testing 0.63156
LogisticNoAge, YOB=7, Testing 0.64016
Champion, YOB=7, Testing 0.63117
LogisticNoAge, YOB=8, Testing 0.63339
Champion, YOB=8, Testing 0.63339
disp(head(DiscData))
ModelID YOB X Y T
_______________ ___ _______ _______ _________
"LogisticNoAge" 1 0 0 0.022711
"LogisticNoAge" 1 0.12062 0.22401 0.022711
"LogisticNoAge" 1 0.23459 0.41435 0.018483
"LogisticNoAge" 1 0.33329 0.59151 0.01722
"LogisticNoAge" 1 0.45578 0.69107 0.01151
"LogisticNoAge" 1 0.5683 0.77452 0.009347
"LogisticNoAge" 1 0.67031 0.84919 0.0087028
"LogisticNoAge" 1 0.78943 0.9063 0.0064814
disp(tail(DiscData))
ModelID YOB X Y T
_______________ ___ _______ ______ __________
"LogisticNoAge" 8 0 0 0.014125
"LogisticNoAge" 8 0.31762 0.5625 0.014125
"LogisticNoAge" 8 0.65751 0.8125 0.0071273
"LogisticNoAge" 8 1 1 0.0040058
"Champion" 8 0 0 0.0040291
"Champion" 8 0.31762 0.5625 0.0040291
"Champion" 8 0.65751 0.8125 0.0017711
"Champion" 8 1 1 0.00086887
Сравните точность этих двух моделей с modelAccuracy.
GroupingVar ="YOB"; [AccMeasure, AccData] = modelAccuracy (pdModel, данные (Ind, :), GroupingVar,'DataID', DataSetChoice,... 'ReferencePD', ChampionPD,'ReferenceID',"Champion"); disp (AccMeasure)
RMSE
__________
LogisticNoAge, grouped by YOB, Testing 0.0031021
Champion, grouped by YOB, Testing 0.00046476
disp(head(AccData))
ModelID YOB PD
__________ ___ _________
"Observed" 1 0.017636
"Observed" 2 0.013303
"Observed" 3 0.010846
"Observed" 4 0.010709
"Observed" 5 0.0093528
"Observed" 6 0.0060197
"Observed" 7 0.0034776
"Observed" 8 0.0012535
disp(tail(AccData))
ModelID YOB PD
__________ ___ _________
"Champion" 1 0.017244
"Champion" 2 0.012999
"Champion" 3 0.011428
"Champion" 4 0.010693
"Champion" 5 0.0085574
"Champion" 6 0.005937
"Champion" 7 0.0035193
"Champion" 8 0.0021802
Используйте modelAccuracyPlot визуализировать точность модели.
modelAccuracyPlot(pdModel,data(Ind,:),GroupingVar,'DataID',DataSetChoice,... 'ReferencePD',ChampionPD,'ReferenceID',"Champion");
[AccMeasure,AccData] = modelAccuracy(pdModel,data(Ind,:),["YOB","ScoreGroup"],'DataID',DataSetChoice,... 'ReferencePD',ChampionPD,'ReferenceID',"Champion"); disp(AccMeasure)
RMSE
_________
LogisticNoAge, grouped by YOB, ScoreGroup, Testing 0.0036974
Champion, grouped by YOB, ScoreGroup, Testing 0.0010716
disp(head(AccData))
ModelID YOB ScoreGroup PD
__________ ___ ___________ _________
"Observed" 1 High Risk 0.030877
"Observed" 1 Medium Risk 0.013541
"Observed" 1 Low Risk 0.0081449
"Observed" 2 High Risk 0.022838
"Observed" 2 Medium Risk 0.012376
"Observed" 2 Low Risk 0.0046482
"Observed" 3 High Risk 0.017651
"Observed" 3 Medium Risk 0.0092652
unstack(AccData,'PD','ModelID')
ans=24×5 table
YOB ScoreGroup Champion LogisticNoAge Observed
___ ___________ _________ _____________ _________
1 High Risk 0.028165 0.019641 0.030877
1 Medium Risk 0.014833 0.0099388 0.013541
1 Low Risk 0.008422 0.0055911 0.0081449
2 High Risk 0.02167 0.019337 0.022838
2 Medium Risk 0.011123 0.0098141 0.012376
2 Low Risk 0.0061856 0.0055194 0.0046482
3 High Risk 0.019285 0.020139 0.017651
3 Medium Risk 0.0098085 0.010179 0.0092652
3 Low Risk 0.0054096 0.0057356 0.005813
4 High Risk 0.018136 0.019175 0.018562
4 Medium Risk 0.0091921 0.0096563 0.0094929
4 Low Risk 0.0050562 0.0054292 0.004392
5 High Risk 0.014818 0.014806 0.016288
5 Medium Risk 0.0072853 0.007454 0.0080033
5 Low Risk 0.0039358 0.0041822 0.0041745
6 High Risk 0.01049 0.012153 0.0096889
⋮
Можно также сравнить две новых разрабатываемых модели.
pdModelTTC = fitLifetimePDModel(data(TrainDataInd,:),"probit",... 'ModelID','ProbitTTC',... 'AgeVar','YOB',... 'IDVar','ID',... 'LoanVars','ScoreGroup',... 'ResponseVar','Default',... 'Description',"TTC model, no macro variables, probit."); disp(pdModelTTC)
Probit with properties:
ModelID: "ProbitTTC"
Description: "TTC model, no macro variables, probit."
Model: [1x1 classreg.regr.CompactGeneralizedLinearModel]
IDVar: "ID"
AgeVar: "YOB"
LoanVars: "ScoreGroup"
MacroVars: ""
ResponseVar: "Default"
Сравните точность.
[AccMeasureTTC,AccDataTTC] = modelAccuracy(pdModelTTC,data(Ind,:),["YOB","ScoreGroup"],'DataID',DataSetChoice,... 'ReferencePD',predict(pdModel,data(Ind,:)),'ReferenceID',pdModel.ModelID); disp(AccMeasureTTC)
RMSE
_________
ProbitTTC, grouped by YOB, ScoreGroup, Testing 0.0016726
LogisticNoAge, grouped by YOB, ScoreGroup, Testing 0.0036974
unstack(AccDataTTC,'PD','ModelID')
ans=24×5 table
YOB ScoreGroup LogisticNoAge Observed ProbitTTC
___ ___________ _____________ _________ _________
1 High Risk 0.019641 0.030877 0.028114
1 Medium Risk 0.0099388 0.013541 0.014865
1 Low Risk 0.0055911 0.0081449 0.0087364
2 High Risk 0.019337 0.022838 0.023239
2 Medium Risk 0.0098141 0.012376 0.012053
2 Low Risk 0.0055194 0.0046482 0.0069786
3 High Risk 0.020139 0.017651 0.019096
3 Medium Risk 0.010179 0.0092652 0.0097145
3 Low Risk 0.0057356 0.005813 0.0055406
4 High Risk 0.019175 0.018562 0.015599
4 Medium Risk 0.0096563 0.0094929 0.0077825
4 Low Risk 0.0054292 0.004392 0.0043722
5 High Risk 0.014806 0.016288 0.012666
5 Medium Risk 0.007454 0.0080033 0.0061971
5 Low Risk 0.0041822 0.0041745 0.0034292
6 High Risk 0.012153 0.0096889 0.010223
⋮
function PD = getChampionModelPDs(data) m = load('LifetimeChampionModel.mat'); PD = predict(m.pdModel,data); end
fitLifetimePDModel | predict | predictLifetime | modelDiscrimination | modelAccuracy | Logistic | Probit | Cox