displaypoints

Return points per predictor per bin

Syntax

PointsInfo = displaypoints(sc)
[PointsInfo,MinScore,MaxScore] = displaypoints(sc)
[PointsInfo,MinScore,MaxScore] = displaypoints(___,Name,Value)

Description

example

PointsInfo = displaypoints(sc) returns a table of points for all bins of all predictor variables used in the creditscorecard object after a linear logistic regression model is fit using fitmodel to the Weight of Evidence data. The PointsInfo table displays information on the predictor name, bin labels, and the corresponding points per bin.

example

[PointsInfo,MinScore,MaxScore] = displaypoints(sc) returns a table of points for all bins of all predictor variables used in the creditscorecard object after a linear logistic regression model is fit (fitmodel) to the Weight of Evidence data. The PointsInfo table displays information on the predictor name, bin labels, and the corresponding points per bin and displaypoints. In addition, the optional MinScore and MaxScore values are returned.

example

[PointsInfo,MinScore,MaxScore] = displaypoints(___,Name,Value) specifies options using one or more name-value pair arguments in addition to the input arguments in the previous syntax.

Examples

collapse all

This example shows how to use displaypoints after a model is fitted to compute the unscaled points per bin, for a given predictor in the creditscorecard model.

Create a creditscorecard object using the CreditCardData.mat file to load the data (using a dataset from Refaat 2011). Use the 'IDVar' argument in the creditscorecard function to indicate that 'CustID' contains ID information and should not be included as a predictor variable.

load CreditCardData 
sc = creditscorecard(data,'IDVar','CustID');

Perform automatic binning to bin for all predictors.

sc = autobinning(sc);

Fit a linear regression model using default parameters.

sc = 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 = 1441.994, Chi2Stat = 5.3511754, PValue = 0.020708306
6. Adding ResStatus, Deviance = 1437.8756, Chi2Stat = 4.118404, PValue = 0.042419078
7. Adding OtherCC, Deviance = 1433.707, Chi2Stat = 4.1686018, PValue = 0.041179769

Generalized linear regression model:
    status ~ [Linear formula with 8 terms in 7 predictors]
    Distribution = Binomial

Estimated Coefficients:
                   Estimate       SE       tStat       pValue  
                   ________    ________    ______    __________

    (Intercept)    0.70239     0.064001    10.975    5.0538e-28
    CustAge        0.60833      0.24932      2.44      0.014687
    ResStatus        1.377      0.65272    2.1097      0.034888
    EmpStatus      0.88565        0.293    3.0227     0.0025055
    CustIncome     0.70164      0.21844    3.2121     0.0013179
    TmWBank         1.1074      0.23271    4.7589    1.9464e-06
    OtherCC         1.0883      0.52912    2.0569      0.039696
    AMBalance        1.045      0.32214    3.2439     0.0011792


1200 observations, 1192 error degrees of freedom
Dispersion: 1
Chi^2-statistic vs. constant model: 89.7, p-value = 1.4e-16

Display unscaled points for predictors retained in the fitting model.

PointsInfo = displaypoints(sc)
PointsInfo=30×3 table
     Predictors           Bin           Points  
    ____________    _______________    _________

    'CustAge'       '[-Inf,33)'         -0.15894
    'CustAge'       '[33,37)'           -0.14036
    'CustAge'       '[37,40)'          -0.060323
    'CustAge'       '[40,46)'           0.046408
    'CustAge'       '[46,48)'            0.21445
    'CustAge'       '[48,58)'            0.23039
    'CustAge'       '[58,Inf]'             0.479
    'ResStatus'     'Tenant'           -0.031252
    'ResStatus'     'Home Owner'         0.12696
    'ResStatus'     'Other'              0.37641
    'EmpStatus'     'Unknown'          -0.076317
    'EmpStatus'     'Employed'           0.31449
    'CustIncome'    '[-Inf,29000)'      -0.45716
    'CustIncome'    '[29000,33000)'     -0.10466
    'CustIncome'    '[33000,35000)'     0.052329
    'CustIncome'    '[35000,40000)'     0.081611
      ⋮

Create a creditscorecard object using the CreditCardData.mat file to load the data with missing values.

load CreditCardData.mat 
head(dataMissing,5)
ans=5×11 table
    CustID    CustAge    TmAtAddress     ResStatus     EmpStatus    CustIncome    TmWBank    OtherCC    AMBalance    UtilRate    status
    ______    _______    ___________    ___________    _________    __________    _______    _______    _________    ________    ______

      1          53          62         <undefined>    Unknown        50000         55         Yes       1055.9        0.22        0   
      2          61          22         Home Owner     Employed       52000         25         Yes       1161.6        0.24        0   
      3          47          30         Tenant         Employed       37000         61         No        877.23        0.29        0   
      4         NaN          75         Home Owner     Employed       53000         20         Yes       157.37        0.08        0   
      5          68          56         Home Owner     Employed       53000         14         Yes       561.84        0.11        0   

fprintf('Number of rows: %d\n',height(dataMissing))
Number of rows: 1200
fprintf('Number of missing values CustAge: %d\n',sum(ismissing(dataMissing.CustAge)))
Number of missing values CustAge: 30
fprintf('Number of missing values ResStatus: %d\n',sum(ismissing(dataMissing.ResStatus)))
Number of missing values ResStatus: 40

Use creditscorecard with the name-value argument 'BinMissingData' set to true to bin the missing numeric or categorical data in a separate bin. Apply automatic binning.

sc = creditscorecard(dataMissing,'IDVar','CustID','BinMissingData',true);
sc = autobinning(sc);

disp(sc)
  creditscorecard with properties:

                GoodLabel: 0
              ResponseVar: 'status'
               WeightsVar: ''
                 VarNames: {1x11 cell}
        NumericPredictors: {1x6 cell}
    CategoricalPredictors: {'ResStatus'  'EmpStatus'  'OtherCC'}
           BinMissingData: 1
                    IDVar: 'CustID'
            PredictorVars: {1x9 cell}
                     Data: [1200x11 table]

Display and plot bin information for numeric data for 'CustAge' that includes missing data in a separate bin labelled <missing>.

[bi,cp] = bininfo(sc,'CustAge');
disp(bi)
        Bin        Good    Bad     Odds       WOE       InfoValue 
    ___________    ____    ___    ______    ________    __________

    '[-Inf,33)'     69      52    1.3269    -0.42156      0.018993
    '[33,37)'       63      45       1.4    -0.36795      0.012839
    '[37,40)'       72      47    1.5319     -0.2779     0.0079824
    '[40,46)'      172      89    1.9326    -0.04556     0.0004549
    '[46,48)'       59      25      2.36     0.15424     0.0016199
    '[48,51)'       99      41    2.4146     0.17713     0.0035449
    '[51,58)'      157      62    2.5323     0.22469     0.0088407
    '[58,Inf]'      93      25      3.72     0.60931      0.032198
    '<missing>'     19      11    1.7273    -0.15787    0.00063885
    'Totals'       803     397    2.0227         NaN      0.087112
plotbins(sc,'CustAge')

Display and plot bin information for categorical data for 'ResStatus' that includes missing data in a separate bin labelled <missing>.

[bi,cg] = bininfo(sc,'ResStatus');
disp(bi)
        Bin         Good    Bad     Odds        WOE       InfoValue 
    ____________    ____    ___    ______    _________    __________

    'Tenant'        296     161    1.8385    -0.095463     0.0035249
    'Home Owner'    352     171    2.0585     0.017549    0.00013382
    'Other'         128      52    2.4615      0.19637     0.0055808
    '<missing>'      27      13    2.0769     0.026469    2.3248e-05
    'Totals'        803     397    2.0227          NaN     0.0092627
plotbins(sc,'ResStatus')

For the 'CustAge' and 'ResStatus' predictors, there is missing data (NaNs and <undefined>) in the training data, and the binning process estimates a WOE value of -0.15787 and 0.026469 respectively for missing data in these predictors, as shown above.

Use fitmodel to fit a logistic regression model using Weight of Evidence (WOE) data. fitmodel internally transforms all the predictor variables into WOE values, using the bins found with the automatic binning process. fitmodel then fits a logistic regression model using a stepwise method (by default). For predictors that have missing data, there is an explicit <missing> bin, with a corresponding WOE value computed from the data. When using fitmodel, the corresponding WOE value for the <missing> bin is applied when performing the WOE transformation.

[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

Display unscaled points for predictors retained in the fitting model (to scale points use formatpoints).

PointsInfo = displaypoints(sc)
PointsInfo=33×3 table
     Predictors          Bin           Points  
    ____________    ______________    _________

    'CustAge'       '[-Inf,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
    'CustIncome'    '[-Inf,29000)'     -0.48396
      ⋮

Notice that points for the <missing> bin for CustAge and ResStatus are explicitly shown. These points are computed from the WOE value for the <missing> bin and the logistic model coefficients.

For predictors that have no missing data in the training set, there is no explicit <missing> bin, and by default the points are set to NaN for missing data, and they lead to a score of NaN when running score. For predictors that have no explicit <missing> bin, use the name-value argument 'Missing' in formatpoints to indicate how missing data should be treated for scoring purposes.

This example shows how to use formatpoints after a model is fitted to format scaled points, and then use displaypoints to display the scaled points per bin, for a given predictor in the creditscorecard model.

Points become scaled when a range is defined. Specifically, a linear transformation from the unscaled to the scaled points is necessary. This transformation is defined either by supplying a shift and slope or by specifying the worst and best scores possible. (For more information, see formatpoints.)

Create a creditscorecard object using the CreditCardData.mat file to load the data (using a dataset from Refaat 2011). Use the 'IDVar' argument in the creditscorecard function to indicate that 'CustID' contains ID information and should not be included as a predictor variable.

load CreditCardData 
sc = creditscorecard(data,'IDVar','CustID');

Perform automatic binning to bin for all predictors.

sc = autobinning(sc);

Fit a linear regression model using default parameters.

sc = 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 = 1441.994, Chi2Stat = 5.3511754, PValue = 0.020708306
6. Adding ResStatus, Deviance = 1437.8756, Chi2Stat = 4.118404, PValue = 0.042419078
7. Adding OtherCC, Deviance = 1433.707, Chi2Stat = 4.1686018, PValue = 0.041179769

Generalized linear regression model:
    status ~ [Linear formula with 8 terms in 7 predictors]
    Distribution = Binomial

Estimated Coefficients:
                   Estimate       SE       tStat       pValue  
                   ________    ________    ______    __________

    (Intercept)    0.70239     0.064001    10.975    5.0538e-28
    CustAge        0.60833      0.24932      2.44      0.014687
    ResStatus        1.377      0.65272    2.1097      0.034888
    EmpStatus      0.88565        0.293    3.0227     0.0025055
    CustIncome     0.70164      0.21844    3.2121     0.0013179
    TmWBank         1.1074      0.23271    4.7589    1.9464e-06
    OtherCC         1.0883      0.52912    2.0569      0.039696
    AMBalance        1.045      0.32214    3.2439     0.0011792


1200 observations, 1192 error degrees of freedom
Dispersion: 1
Chi^2-statistic vs. constant model: 89.7, p-value = 1.4e-16

Use the formatpoints function to scale providing the 'Worst' and 'Best' score values. The range provided below is a common score range.

sc = formatpoints(sc,'WorstAndBestScores',[300 850]);

Display the points information again to verify that the points are now scaled and also display the scaled minimum and maximum scores.

[PointsInfo,MinScore,MaxScore] = displaypoints(sc)
PointsInfo=30×3 table
     Predictors           Bin          Points
    ____________    _______________    ______

    'CustAge'       '[-Inf,33)'        46.396
    'CustAge'       '[33,37)'          48.727
    'CustAge'       '[37,40)'          58.772
    'CustAge'       '[40,46)'          72.167
    'CustAge'       '[46,48)'          93.256
    'CustAge'       '[48,58)'          95.256
    'CustAge'       '[58,Inf]'         126.46
    'ResStatus'     'Tenant'           62.421
    'ResStatus'     'Home Owner'       82.276
    'ResStatus'     'Other'            113.58
    'EmpStatus'     'Unknown'          56.765
    'EmpStatus'     'Employed'         105.81
    'CustIncome'    '[-Inf,29000)'     8.9706
    'CustIncome'    '[29000,33000)'    53.208
    'CustIncome'    '[33000,35000)'     72.91
    'CustIncome'    '[35000,40000)'    76.585
      ⋮

MinScore = 300.0000
MaxScore = 850

Notice that, as expected, the values of MinScore and MaxScore correspond to the worst and best possible scores.

This example shows how to use displaypoints after a model is fitted to separate the base points from the rest of the points assigned to each predictor variable. The name-value pair argument 'BasePoints' in the formatpoints function is a boolean that serves this purpose. By default, the base points are spread across all variables in the scorecard.

Create a creditscorecard object using the CreditCardData.mat file to load the data (using a dataset from Refaat 2011). Use the 'IDVar' argument in the creditscorecard function to indicate that 'CustID' contains ID information and should not be included as a predictor variable.

load CreditCardData 
sc = creditscorecard(data,'IDVar','CustID');

Perform automatic binning to bin for all predictors.

sc = autobinning(sc);

Fit a linear regression model using default parameters.

sc = 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 = 1441.994, Chi2Stat = 5.3511754, PValue = 0.020708306
6. Adding ResStatus, Deviance = 1437.8756, Chi2Stat = 4.118404, PValue = 0.042419078
7. Adding OtherCC, Deviance = 1433.707, Chi2Stat = 4.1686018, PValue = 0.041179769

Generalized linear regression model:
    status ~ [Linear formula with 8 terms in 7 predictors]
    Distribution = Binomial

Estimated Coefficients:
                   Estimate       SE       tStat       pValue  
                   ________    ________    ______    __________

    (Intercept)    0.70239     0.064001    10.975    5.0538e-28
    CustAge        0.60833      0.24932      2.44      0.014687
    ResStatus        1.377      0.65272    2.1097      0.034888
    EmpStatus      0.88565        0.293    3.0227     0.0025055
    CustIncome     0.70164      0.21844    3.2121     0.0013179
    TmWBank         1.1074      0.23271    4.7589    1.9464e-06
    OtherCC         1.0883      0.52912    2.0569      0.039696
    AMBalance        1.045      0.32214    3.2439     0.0011792


1200 observations, 1192 error degrees of freedom
Dispersion: 1
Chi^2-statistic vs. constant model: 89.7, p-value = 1.4e-16

Use the formatpoints function to separate the base points by providing the 'BasePoints' name-value pair argument.

sc = formatpoints(sc,'BasePoints',true);

Display the base points, separated out from the other points, for predictors retained in the fitting model.

PointsInfo = displaypoints(sc)
PointsInfo=31×3 table
     Predictors           Bin           Points  
    ____________    _______________    _________

    'BasePoints'    'BasePoints'         0.70239
    'CustAge'       '[-Inf,33)'         -0.25928
    'CustAge'       '[33,37)'           -0.24071
    'CustAge'       '[37,40)'           -0.16066
    'CustAge'       '[40,46)'          -0.053933
    'CustAge'       '[46,48)'            0.11411
    'CustAge'       '[48,58)'            0.13005
    'CustAge'       '[58,Inf]'           0.37866
    'ResStatus'     'Tenant'            -0.13159
    'ResStatus'     'Home Owner'        0.026616
    'ResStatus'     'Other'              0.27607
    'EmpStatus'     'Unknown'           -0.17666
    'EmpStatus'     'Employed'           0.21415
    'CustIncome'    '[-Inf,29000)'       -0.5575
    'CustIncome'    '[29000,33000)'       -0.205
    'CustIncome'    '[33000,35000)'    -0.048013
      ⋮

This example shows how to use displaypoints after a model is fitted and the modifybins function is used to provide user-defined bin labels for a numeric predictor.

Create a creditscorecard object using the CreditCardData.mat file to load the data (using a dataset from Refaat 2011). Use the 'IDVar' argument in the creditscorecard function to indicate that 'CustID' contains ID information and should not be included as a predictor variable.

load CreditCardData 
sc = creditscorecard(data,'IDVar','CustID');

Perform automatic binning to bin for all predictors.

sc = autobinning(sc);

Fit a linear regression model using default parameters.

sc = 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 = 1441.994, Chi2Stat = 5.3511754, PValue = 0.020708306
6. Adding ResStatus, Deviance = 1437.8756, Chi2Stat = 4.118404, PValue = 0.042419078
7. Adding OtherCC, Deviance = 1433.707, Chi2Stat = 4.1686018, PValue = 0.041179769

Generalized linear regression model:
    status ~ [Linear formula with 8 terms in 7 predictors]
    Distribution = Binomial

Estimated Coefficients:
                   Estimate       SE       tStat       pValue  
                   ________    ________    ______    __________

    (Intercept)    0.70239     0.064001    10.975    5.0538e-28
    CustAge        0.60833      0.24932      2.44      0.014687
    ResStatus        1.377      0.65272    2.1097      0.034888
    EmpStatus      0.88565        0.293    3.0227     0.0025055
    CustIncome     0.70164      0.21844    3.2121     0.0013179
    TmWBank         1.1074      0.23271    4.7589    1.9464e-06
    OtherCC         1.0883      0.52912    2.0569      0.039696
    AMBalance        1.045      0.32214    3.2439     0.0011792


1200 observations, 1192 error degrees of freedom
Dispersion: 1
Chi^2-statistic vs. constant model: 89.7, p-value = 1.4e-16

Use the displaypoints function to display point information.

[PointsInfo,MinScore,MaxScore] = displaypoints(sc)
PointsInfo=30×3 table
     Predictors           Bin           Points  
    ____________    _______________    _________

    'CustAge'       '[-Inf,33)'         -0.15894
    'CustAge'       '[33,37)'           -0.14036
    'CustAge'       '[37,40)'          -0.060323
    'CustAge'       '[40,46)'           0.046408
    'CustAge'       '[46,48)'            0.21445
    'CustAge'       '[48,58)'            0.23039
    'CustAge'       '[58,Inf]'             0.479
    'ResStatus'     'Tenant'           -0.031252
    'ResStatus'     'Home Owner'         0.12696
    'ResStatus'     'Other'              0.37641
    'EmpStatus'     'Unknown'          -0.076317
    'EmpStatus'     'Employed'           0.31449
    'CustIncome'    '[-Inf,29000)'      -0.45716
    'CustIncome'    '[29000,33000)'     -0.10466
    'CustIncome'    '[33000,35000)'     0.052329
    'CustIncome'    '[35000,40000)'     0.081611
      ⋮

MinScore = -1.3100
MaxScore = 3.0726

Use the modifybins function to specify user-defined bin labels for 'CustAge' so that the bin ranges are described in natural language.

labels = {'Up to 32','33 to 36','37 to 39','40 to 45','46 to 47','48 to 57','At least 58'};
sc = modifybins(sc,'CustAge','BinLabels',labels);

Rerun displaypoints to verify the updated bin labels.

[PointsInfo,MinScore,MaxScore] = displaypoints(sc)
PointsInfo=30×3 table
     Predictors           Bin           Points  
    ____________    _______________    _________

    'CustAge'       'Up to 32'          -0.15894
    'CustAge'       '33 to 36'          -0.14036
    'CustAge'       '37 to 39'         -0.060323
    'CustAge'       '40 to 45'          0.046408
    'CustAge'       '46 to 47'           0.21445
    'CustAge'       '48 to 57'           0.23039
    'CustAge'       'At least 58'          0.479
    'ResStatus'     'Tenant'           -0.031252
    'ResStatus'     'Home Owner'         0.12696
    'ResStatus'     'Other'              0.37641
    'EmpStatus'     'Unknown'          -0.076317
    'EmpStatus'     'Employed'           0.31449
    'CustIncome'    '[-Inf,29000)'      -0.45716
    'CustIncome'    '[29000,33000)'     -0.10466
    'CustIncome'    '[33000,35000)'     0.052329
    'CustIncome'    '[35000,40000)'     0.081611
      ⋮

MinScore = -1.3100
MaxScore = 3.0726

This example shows how to use a credit scorecard to compute the weights of the predictors. The weights of the predictors are determined from the range of points of each predictor, divided by the total range of points for the scorecard. The points for the scorecard not only take into consideration the betas, but also implicitly the binning of the predictor values and the corresponding weights of evidence.

Create a scorecard.

load CreditCardData.mat
sc = creditscorecard(data,'IDVar','CustID');
sc = autobinning(sc);
sc = 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 = 1441.994, Chi2Stat = 5.3511754, PValue = 0.020708306
6. Adding ResStatus, Deviance = 1437.8756, Chi2Stat = 4.118404, PValue = 0.042419078
7. Adding OtherCC, Deviance = 1433.707, Chi2Stat = 4.1686018, PValue = 0.041179769

Generalized linear regression model:
    status ~ [Linear formula with 8 terms in 7 predictors]
    Distribution = Binomial

Estimated Coefficients:
                   Estimate       SE       tStat       pValue  
                   ________    ________    ______    __________

    (Intercept)    0.70239     0.064001    10.975    5.0538e-28
    CustAge        0.60833      0.24932      2.44      0.014687
    ResStatus        1.377      0.65272    2.1097      0.034888
    EmpStatus      0.88565        0.293    3.0227     0.0025055
    CustIncome     0.70164      0.21844    3.2121     0.0013179
    TmWBank         1.1074      0.23271    4.7589    1.9464e-06
    OtherCC         1.0883      0.52912    2.0569      0.039696
    AMBalance        1.045      0.32214    3.2439     0.0011792


1200 observations, 1192 error degrees of freedom
Dispersion: 1
Chi^2-statistic vs. constant model: 89.7, p-value = 1.4e-16

Compute scorecard points and the MinPts and MaxPts scores.

sc = formatpoints(sc,'PointsOddsAndPDO',[500 2 50]);
[PointsTable,MinPts,MaxPts] = displaypoints(sc);
PtsRange = MaxPts-MinPts;
disp(PointsTable(1:10,:)); 
    Predictors         Bin         Points
    ___________    ____________    ______

    'CustAge'      '[-Inf,33)'     52.821
    'CustAge'      '[33,37)'       54.161
    'CustAge'      '[37,40)'       59.934
    'CustAge'      '[40,46)'       67.633
    'CustAge'      '[46,48)'       79.755
    'CustAge'      '[48,58)'       80.905
    'CustAge'      '[58,Inf]'      98.838
    'ResStatus'    'Tenant'        62.031
    'ResStatus'    'Home Owner'    73.444
    'ResStatus'    'Other'         91.438
 fprintf('Min points: %g, Max points: %g\n',MinPts,MaxPts); 
Min points: 355.505, Max points: 671.64

Compute the predictor weights.

Predictor = unique(PointsTable.Predictors,'stable');
NumPred = length(Predictor);
Weight = zeros(NumPred,1);
for ii=1:NumPred
   Ind = cellfun(@(x)strcmpi(Predictor{ii},x),PointsTable.Predictors);
   MaxPtsPred = max(PointsTable.Points(Ind));
   MinPtsPred = min(PointsTable.Points(Ind));
   Weight(ii) = 100*(MaxPtsPred-MinPtsPred)/PtsRange;
end

PredictorWeights = table(Predictor,Weight);
PredictorWeights(end+1,:) = PredictorWeights(end,:);
PredictorWeights.Predictor{end} = 'Total';
PredictorWeights.Weight(end) = sum(Weight);
disp(PredictorWeights)
     Predictor      Weight
    ____________    ______

    'CustAge'       14.556
    'ResStatus'      9.302
    'EmpStatus'     8.9174
    'CustIncome'    20.401
    'TmWBank'       25.884
    'OtherCC'       7.9885
    'AMBalance'     12.951
    'Total'            100

The weights are defined as the range of points for the predictor divided by the range of points for the scorecard.

To create a creditscorecard object using the CreditCardData.mat file, load the data (using a dataset from Refaat 2011). Using the dataMissing dataset, set the 'BinMissingData' indicator to true.

load CreditCardData.mat 
sc = creditscorecard(dataMissing,'BinMissingData',true); 

Use autobinning with the creditscorecard object.

sc = autobinning(sc);

The binning map or rules for categorical data are summarized in a "category grouping" table, returned as an optional output. By default, each category is placed in a separate bin. Here is the information for the predictor ResStatus.

[bi,cg] = bininfo(sc,'ResStatus')
bi=5×6 table
        Bin         Good    Bad     Odds        WOE       InfoValue 
    ____________    ____    ___    ______    _________    __________

    'Tenant'        296     161    1.8385    -0.095463     0.0035249
    'Home Owner'    352     171    2.0585     0.017549    0.00013382
    'Other'         128      52    2.4615      0.19637     0.0055808
    '<missing>'      27      13    2.0769     0.026469    2.3248e-05
    'Totals'        803     397    2.0227          NaN     0.0092627

cg=3×2 table
      Category      BinNumber
    ____________    _________

    'Tenant'            1    
    'Home Owner'        2    
    'Other'             3    

To group categories 'Tenant' and 'Other', modify the category grouping table cg, so the bin number for 'Other' is the same as the bin number for 'Tenant'. Then use modifybins to update the creditscorecard object.

cg.BinNumber(3) = 2; 
sc = modifybins(sc,'ResStatus','Catg',cg); 

Display the updated bin information using bininfo. Note that the bin labels has been updated and that the bin membership information is contained in the category grouping cg.

[bi,cg] = bininfo(sc,'ResStatus')
bi=4×6 table
        Bin        Good    Bad     Odds        WOE       InfoValue 
    ___________    ____    ___    ______    _________    __________

    'Group1'       296     161    1.8385    -0.095463     0.0035249
    'Group2'       480     223    2.1525     0.062196     0.0022419
    '<missing>'     27      13    2.0769     0.026469    2.3248e-05
    'Totals'       803     397    2.0227          NaN       0.00579

cg=3×2 table
      Category      BinNumber
    ____________    _________

    'Tenant'            1    
    'Home Owner'        2    
    'Other'             2    

Use formatpoints with the 'Missing' name-value pair argument to indicate that missing data is assigned 'maxpoints'.

sc = formatpoints(sc,'BasePoints',true,'Missing','maxpoints','WorstAndBest',[300 800]); 

Use fitmodel to fit the model.

sc = fitmodel(sc,'VariableSelection','fullmodel','Display','Off'); 

Then use displaypoints with the creditscorecard object to return a table of points for all bins of all predictor variables used in the compactCreditScorecard object. By setting the displaypoints name-value pair argument for 'ShowCategoricalMembers' to true, all the members contained in each individual group are displayed.

[PointsInfo,MinScore,MaxScore] = displaypoints(sc,'ShowCategoricalMembers',true)
PointsInfo=43×3 table
     Predictors          Bin         Points 
    _____________    ____________    _______

    'BasePoints'     'BasePoints'     535.25
    'CustID'         '[-Inf,121)'     12.085
    'CustID'         '[121,241)'      5.4738
    'CustID'         '[241,1081)'    -1.4061
    'CustID'         '[1081,Inf]'    -7.2217
    'CustAge'        '[-Inf,33)'     -25.973
    'CustAge'        '[33,37)'        -22.67
    'CustAge'        '[37,40)'       -17.122
    'CustAge'        '[40,46)'       -2.8071
    'CustAge'        '[46,48)'        9.5034
    'CustAge'        '[48,51)'        10.913
    'CustAge'        '[51,58)'        13.844
    'CustAge'        '[58,Inf]'       37.541
    'CustAge'        '<missing>'     -9.7271
    'TmAtAddress'    '[-Inf,23)'     -9.3683
    'TmAtAddress'    '[23,83)'        2.4156
      ⋮

MinScore = 300.0000
MaxScore = 800

Input Arguments

collapse all

Credit scorecard model, specified as a creditscorecard object. Use creditscorecard to create a creditscorecard object.

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: [PointsInfo,MinScore,MaxScore] = displaypoints(sc,‘ShowCategoricalMembers’,true)

Indicator for how to display bins labels of categories that were grouped together, specified as the comma-separated pair consisting of 'ShowCategoricalMembers' and a logical scalar with a value of true or false.

By default, when 'ShowCategoricalMembers' is false, bin labels are displayed as Group1, Group2,…,Groupn, or if the bin labels were modified in creditscorecard, then the user-defined bin label names are displayed.

If 'ShowCategoricalMembers' is true, all the members contained in each individual group are displayed.

Data Types: logical

Output Arguments

collapse all

One row per bin, per predictor, with the corresponding points, returned as a table. For example:

PredictorsBinPoints
Predictor_1Bin_11Points_11
 ......
Predictor_2Bin_21Points_21
 ......
Predictor_jBin_jiPoints_ji
 ......

When base points are reported separately (see formatpoints), the first row of the returned PointsInfo table contains the base points.

Minimum possible total score, returned as a scalar.

Note

Minimum score is the lowest possible total score in the mathematical sense, independently of whether a low score means high risk or low risk.

Maximum possible total score, returned as a scalar.

Note

Maximum score is the highest possible total score in the mathematical sense, independently of whether a high score means high risk or low risk.

Algorithms

The points for predictor j and bin i are, by default, given by

Points_ji = (Shift + Slope*b0)/p + Slope*(bj*WOEj(i))
where bj is the model coefficient of predictor j, p is the number of predictors in the model, and WOEj(i) is the Weight of Evidence (WOE) value for the i-th bin corresponding to the j-th model predictor. Shift and Slope are scaling constants.

When the base points are reported separately (see the formatpoints name-value pair argument BasePoints), the base points are given by

Base Points = Shift + Slope*b0,
and the points for the j-th predictor, i-th row are given by
Points_ji = Slope*(bj*WOEj(i))).

By default, the base points are not reported separately.

The minimum and maximum scores are:

MinScore = Shift + Slope*b0 + min(Slope*b1*WOE1) + ... +min(Slope*bp*WOEp)),
MaxScore = Shift + Slope*b0 + max(Slope*b1*WOE1) + ... +max(Slope*bp*WOEp)).

Use formatpoints to control the way points are scaled, rounded, and whether the base points are reported separately. See formatpoints for more information on format parameters and for details and formulas on these formatting options.

References

[1] Anderson, R. The Credit Scoring Toolkit. Oxford University Press, 2007.

[2] Refaat, M. Credit Risk Scorecards: Development and Implementation Using SAS. lulu.com, 2011.

Introduced in R2014b