主要内容
compactCreditScorecard对象工作流
这个例子展示了一个创建compactCreditScorecard对象从一个信用记分卡对象。
步骤1.创建一个信用记分卡对象
创建一个compactCreditScorecard对象，则必须首先创建信用记分卡对象。创建一个信用记分卡对象的CreditCardData.mat文件，并设置名称-值对参数“BinMissingData”到真正的因为dataMissing数据集包含丢失的数据。
负载CreditCardData.matsc = creditscorecard (dataMissing,“IDVar”,“CustID”,“BinMissingData”，对）；sc=自动绕线（sc）；sc=修改箱（sc，“CustAge”,“MinValue”, 0);sc = modifybins (sc,“收入”,“MinValue”, 0);
第二步。为模型拟合一个逻辑回归模型信用记分卡对象
使用fitmodel利用证据权重(WOE)数据拟合逻辑回归模型。
[sc，mdl]=fitmodel（sc）；
1.加上CustIncome，偏差=1490.8527，Chi2Stat=32.588614，PValue=1.1387992e-08 2。添加TmWBank，偏差=1467.1415，Chi2Stat=23.711203，PValue=1.1192909e-06 3。添加AMBalance，偏差=1455.5715，Chi2Stat=11.569967，PValue=0.00067025601 4。添加EmpStatus，偏差=1447.3451，Chi2Stat=8.2264038，PValue=0.0041285257 5。加上保管费，偏差=1442.8477，Chi2Stat=4.4974731，PValue=0.033944979 6。加上ResStatus，偏差=1438.9783，Chi2Stat=3.86941，PValue=0.049173805 7。加上其他Cc，偏差=1434.9751，Chi2Stat=4.0031966，PValue=0.045414057广义线性回归模型：状态~[7个预测值中包含8项的线性公式]分布=二项式估计系数：估计统计PValue uuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuu0.70229 0.063959 10.98 4.7498e-28保管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保管收入0.73535 0.2159 3.406 0.00065929 TmWBank 1.1065 0.23267 4.7556 1.9783e-06其他CC 1.0648 0.52826 2.0156 0.043846安巴兰斯1.2497观察结果，1192误差自由度离散度：1 Chi^2-统计与常数模型：88.5，p值=2.55e-16
步骤3。创建一个新的数据集来评分信用记分卡对象
创建一个新的数据集，用于基于先前创建的数据集进行评分信用记分卡对象。
tdata = data(1:10, mdl.PredictorNames);tdata.CustAge(2) =南;tdata.CustAge (5) = 5;tdata.ResStatus (1) =' <定义> '; tdata.ResStatus（3）=“房东”; tdata.EMP状态（3）=' <定义> '；tdata.CustIncome（4）=NaN；tdata.EmpStatus（7）=“自由职业者”；tdata.CustIncome (8) = 1;tdata.CustIncome(4) =南;disp (tdata);
（UUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUU（UUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUU雇佣NaN 20是157.37-5个业主雇佣53000 14是561.8465业主雇佣48000 59是968.18 34业主自由职业者32000 26是717.82 50其他雇佣-1 33否3041.2 50租户未知52000 25是115.56 49业主未知53000 23是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(数据）.然后使用probdefault有了新数据(数据)计算违约概率。使用时格式点这个“失踪”名称-值对参数设置为“minpoints”因为数据包含丢失的数据。
PointsInfo = 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 '}{''} 0.0096749 {'ResStatus'} {'Tenant'} -0.029778 {'ResStatus'} {'Home Owner'} 0.12425 {'ResStatus'} {'Other'} 0.36796 {'ResStatus'} {' '} 0.1364 {'EmpStatus'} {'Unknown'} -0.075948 {'EmpStatus'} {'Employed'} 0.31401 {'EmpStatus'} {' '}⋮
[score, Points] = score(sc, tdata)
成绩=10×11.2784 1.0071南南0.9960 1.8771南南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 0.0096749 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 -0.0447010.15715 0.35539 0.0096749 0.12425 0.31401 0.45309 - 0.04701 0.15715 -0.017438 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 -0.017438 0.20204 0.12425 -0.075948 0.45309-0.033652 0.15715 -0.017438
Pd = probdefault(sc, tdata)
pd =10×10.2178 0.2676楠楠0.2697 0.1327楠楠0.2634 0.3080
sc=格式点（sc，“基点”符合事实的“失踪”,“minpoints”,“圆形”,“最终核心”,“PointsOddsAndPDO”, 500, 2, 50);PointsInfo1 = displaypoints (sc)
点sinfo1=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 '}{'[58,Inf]'} 25.238 {'CustAge'} {''} -6.5392 {'ResStatus'} {'Tenant'} -9.3852 {'ResStatus'} {'Home Owner'} 1.7253 {'ResStatus'} {'Other'} 19.305 {'ResStatus'} {' '} 2.6022 {'EmpStatus'} {'Unknown'} -12.716 {'EmpStatus'} {'Employed'} 15.414⋮
[Scores1, Points1] = score(sc, tdata)
得分1=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 15.414 25.446 -9.6646 4.0988 -8.495 500.66 7.3367 1.725319.305 15.414 -42.148 -9.6646 -20.609 -22.526 500.66 7.3367 -9.3852 -12.716 25.446 -9.6646 4.0988 -8.495 500.66 7.3367 1.3367-12.716 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
第四步。创建一个compactCreditScorecard对象的信用记分卡对象
创建一个compactCreditScorecard使用信用记分卡对象作为输入。或者，您可以创建compactCreditScorecard使用紧凑的函数在Financial Toolbox™中。
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}
第五步。使用关联函数分析compactCreditScorecard对象
您可以使用分析compactCreditScorecard对象displaypoints,分数,probdefault来自风险管理工具箱™.
PointsInfo2 = displaypoints (csc)
点sinfo2=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 '}{'[58,Inf]'} 25.238 {'CustAge'} {''} -6.5392 {'ResStatus'} {'Tenant'} -9.3852 {'ResStatus'} {'Home Owner'} 1.7253 {'ResStatus'} {'Other'} 19.305 {'ResStatus'} {' '} 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 15.414 25.446 -9.6646 4.0988 -8.495 500.66 7.3367 1.725319.305 15.414 -42.148 -9.6646 -20.609 -22.526 500.66 7.3367 -9.3852 -12.716 25.446 -9.6646 4.0988 -8.495 500.66 7.3367 1.3367-12.716 25.446 -9.6646 4.0988 -8.495
pd2=默认值（csc、tdata）
pd2 =10×10.2178 0.2676 0.3721 0.3488 0.2697 0.1327 0.5178 0.5077 0.2634 0.3080
比较两个文件的大小信用记分卡和compactCreditScorecard对象。
谁(“dataMissing”,“sc”,csc的)
类属性名称大小字节csc 39598 1 x1 compactCreditScorecard dataMissing sc 1 x1 166575 creditscorecard 1200 x11 84603表
尺寸compactCreditScorecard对象是轻量级的信用记分卡对象。然而,compactCreditScorecard对象不能直接修改。如果你需要换衣服compactCreditScorecard对象，则必须更改起始值信用记分卡对象，然后重新转换该对象以创建compactCreditScorecard对象了。
另见
信用记分卡|screenpredictors|autobinning|bininfo|预测信息|modifypredictor|改装箱|bindata|plotbins|fitmodel|displaypoints|格式点|分数|setmodel|probdefault|validatemodel
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