compactCreditScorecard对象的工作流
此示例显示了创建对象的工作流compactCreditScorecard对象的creditscorecard对象。
步骤1。创建一个creditscorecard对象
要创建compactCreditScorecard对象时，必须首先创建creditscorecard对象。创建一个creditscorecard对象的CreditCardData.mat文件，并设置名称-值对参数“BinMissingData”来真正的因为dataMissing数据集包含缺失数据。
负载CreditCardData.matsc =信用记分卡(数据丢失，“IDVar”，“CustID”，“BinMissingData”,真正的);Sc = autobinning(Sc);Sc = modifybins(Sc，“CustAge”，“MinValue”, 0);Sc = modifybins(Sc，“CustIncome”，“MinValue”, 0);
步骤2。拟合logistic回归模型creditscorecard对象
使用fitmodel使用证据权重(WOE)数据拟合逻辑回归模型。
[sc, mdl] = fitmodel(sc);
1.添加CustIncome, Deviance = 1490.8527, Chi2Stat = 32.588614, PValue = 1.1387992e-08添加TmWBank, Deviance = 1467.1415, Chi2Stat = 23.711203, PValue = 1.1192909e-063 .添加AMBalance, Deviance = 1455.5715, Chi2Stat = 11.569967, PValue = 0.00067025601。5.添加EmpStatus, Deviance = 1447.3451, Chi2Stat = 8.2264038, PValue = 0.0041285257。添加CustAge, Deviance = 1442.8477, Chi2Stat = 4.4974731, PValue = 0.0339449797.添加ResStatus, Deviance = 1438.9783, Chi2Stat = 3.86941, PValue = 0.049173805。添加OtherCC, Deviance = 1434.9751, Chi2Stat = 4.0031966, PValue = 0.045414057广义线性回归模型:状态~[7个预测因子中有8项的线性公式]分布=二项估计系数:估计SE tStat pValue ________ ________ ______ __________(拦截)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观测值，1192误差自由度离散度:1 Chi^2统计与常量模型:88.5, p-value = 2.55e-16
步骤3。创建用于评分的新数据集creditscorecard对象
创建一个新的数据集，用于根据先前创建的数据进行评分creditscorecard对象。
tdata = data(1:10, mdl.PredictorNames);tdata.CustAge(2) = NaN;tdata.CustAge(5) = -5;tdata.ResStatus (1) =' <定义> '；tdata.ResStatus (3) =“房东”；tdata.EmpStatus (3) =' <定义> '；tdata.CustIncome(4) = NaN;tdata.EmpStatus (7) =“自由职业者”；tdata.CustIncome(8) = -1;tdata.CustIncome(4) = NaN;disp (tdata);
CustAge ResStatus EmpStatus CustIncome TmWBank OtherCC AMBalance _______ ___________ ___________ __________ _______ _______ _________ 53 <定义>未知50000 55是的1055.9南业主雇用了52000名25 1161.6 47房东<未定义> 37000 61没有877.23业主雇佣南20是的业主雇用了53000名14是的561.84 157.37 65业主雇用了48000名59岁是的968.18 34业主自由职业者32000 26是的717.82其他50雇佣1 33不3041.2 50租户未知52000 25是的115.56 49 Home Owner Unknown 53000 23 Yes 718.5
使用displaypoints显示每个预测器的点数。使用分数使用新数据计算信用评分(tdata)．然后使用probdefault根据新数据(tdata)来计算违约的概率。当使用formatpoints,“失踪”名称-值对参数设置为“minpoints”因为tdata包含丢失的数据。
积分信息= displaypoints(sc)
PointsInfo =38×3表预测本点 _____________ ______________ _________ {' CustAge’}{[0,33)的-0.14173}{‘CustAge}{[33岁,37)的-0.11095}{‘CustAge}{[37、40)的-0.059244}{‘CustAge}{[40岁,46)的0.074167}{‘CustAge}{[46岁,48)的0.1889}{‘CustAge}{[48, 51)的0.20204}{‘CustAge}{[51岁,58)的0.22935}{‘CustAge}{[58岁的Inf]的}0.45019{‘CustAge}{‘失踪> <}0.0096749{‘ResStatus}{“租户”}-0.029778{‘ResStatus}{‘业主’}0.12425{‘ResStatus}{‘其他’}0.36796 {' ResStatus '}{'<缺少>'}0.1364 {'EmpStatus'}{'未知'}-0.075948 {'EmpStatus'}{'已雇用'}0.31401 {'EmpStatus'}{'<缺少>'}NaN `
[score, Points] = score(sc, tdata)
成绩=10×11.2784 1.0071 NaN NaN 0.9960 1.8771 NaN NaN 1.0283 0.8095
点=10×7表CustAge ResStatus EmpStatus CustIncome TmWBank OtherCC AMBalance _________ _________ _________ __________ _________ ________ _________ 0.22935 0.1364 -0.075948 0.45309 0.3958 0.15715 -0.017438 0.15715 -0.017438 0.1889 0.1364 NaN 0.080697 0.3958 -0.18537 -0.017438 0.20204 0.12425 0.31401 0.45309 -0.044701 0.15715 0.45309 0.3958 0.15715 -0.017438 -0.11095 0.12425NaN -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 =10×10.2178 0.2676 NaN NaN 0.2697 0.1327 NaN NaN 0.2634 0.3080
Sc = formatpoints(Sc，“BasePoints”,真的,“失踪”，“minpoints”，“圆”，“finalscore”，“PointsOddsAndPDO”，[500, 2, 50]);PointsInfo1 = displaypoints(sc)
PointsInfo1 =39×3表预测本点 ______________ ______________ _______ {' BasePoints}{‘BasePoints} 500.66{‘CustAge}{-17.461的[0,33)}{‘CustAge}{[33岁,37)的-15.24{‘CustAge}}{[37、40)的-11.511}{“CustAge”}{'[40岁,46)}-1.8871{‘CustAge}{6.3888[46, 48)}{‘CustAge}{[48, 51)的7.3367}{“CustAge”}{[51岁,58)的9.3068}{‘CustAge}{的[58岁的Inf]} 25.238{‘CustAge}{‘失踪> <}-6.5392{‘ResStatus}{“租户”}-9.3852{‘ResStatus}{‘业主’}1.7253{‘ResStatus}{'其他'}19.305 {'ResStatus'}{'<缺失>'}2.6022 {'EmpStatus'} {'Unknown'} -12.716 {'EmpStatus'} {'Employed'} 15.414
[Scores1, Points1] = score(sc, tdata)
Scores1 =10×1542 523 488 495 522 585 445 448 524 508
里=10×8表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 15.446 -9.6646 4.0988 -8.495 500.66 6.3888 2.6022 -12.716 -1.4161 21.314 -20.609 - 8.4161 21.314 - 20.3367 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 4.0914 4.0988 4.0988 -8.495500.66 -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 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 =10×10.2178 0.2676 0.3721 0.3488 0.2697 0.1327 0.5178 0.5077 0.2634 0.3080
步骤4。创建一个compactCreditScorecard对象的creditscorecard对象
创建一个compactCreditScorecard对象使用creditscorecard对象作为输入。或者，您可以创建compactCreditScorecard对象使用紧凑的在财务工具箱™中的功能。
csc = compactCreditScorecard(sc)
csc = compactCreditScorecard属性:描述:“GoodLabel: 0 ResponseVar: 'status' WeightsVar:”NumericPredictors: {'CustAge' 'CustIncome' 'TmWBank' 'AMBalance'}类别predictors: {'ResStatus' 'EmpStatus' 'OtherCC'} PredictorVars: {1x7 cell}
第5步。使用关联函数来分析compactCreditScorecard对象
可以使用。分析compactCreditScorecard对象displaypoints，分数,probdefault来自风险管理工具箱™。
PointsInfo2 = displaypoints(csc)
PointsInfo2 =39×3表预测本点 ______________ ______________ _______ {' BasePoints}{‘BasePoints} 500.66{‘CustAge}{-17.461的[0,33)}{‘CustAge}{[33岁,37)的-15.24{‘CustAge}}{[37、40)的-11.511}{“CustAge”}{'[40岁,46)}-1.8871{‘CustAge}{6.3888[46, 48)}{‘CustAge}{[48, 51)的7.3367}{“CustAge”}{[51岁,58)的9.3068}{‘CustAge}{的[58岁的Inf]} 25.238{‘CustAge}{‘失踪> <}-6.5392{‘ResStatus}{“租户”}-9.3852{‘ResStatus}{‘业主’}1.7253{‘ResStatus}{'其他'}19.305 {'ResStatus'}{'<缺失>'}2.6022 {'EmpStatus'} {'Unknown'} -12.716 {'EmpStatus'} {'Employed'} 15.414
[Scores2, Points2] = score(csc, tdata)
Scores2 =10×1542 523 488 495 522 585 445 448 524 508
Points2 =10×8表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 15.446 -9.6646 4.0988 -8.495 500.66 6.3888 2.6022 -12.716 -1.4161 21.314 -20.609 - 8.4161 21.314 - 20.3367 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 4.0914 4.0988 4.0988 -8.495500.66 -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 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 =10×10.2178 0.2676 0.3721 0.3488 0.2697 0.1327 0.5178 0.5077 0.2634 0.3080
比较的大小creditscorecard而且compactCreditScorecard对象。
谁(“dataMissing”，“sc”，csc的）
名称大小字节类属性csc 1x1 39598 compactCreditScorecard dataMissing 1200x11 84603表sc 1x1 166575 creditscorecard
的大小compactCreditScorecard对象是轻量级的creditscorecard对象。然而,compactCreditScorecard对象不能直接修改。如果您需要更改acompactCreditScorecard对象，必须更改起始creditscorecard对象，然后重新转换该对象以创建compactCreditScorecard对象了。
另请参阅
creditscorecard|screenpredictors|autobinning|bininfo|predictorinfo|modifypredictor|modifybins|bindata|plotbins|fitmodel|displaypoints|formatpoints|分数|setmodel|probdefault|validatemodel
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