displaypoints
Return points per predictor per bin
Syntax
Description
returns a table of points for all bins of all predictor variables used in the
PointsInfo
= displaypoints(sc
)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.
[
returns a table of points for all bins of all predictor variables used in the
PointsInfo
,MinScore
,MaxScore
]
= displaypoints(sc
)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.
[
specifies options using one or more name-value pair arguments in addition to the
input arguments in the previous syntax. PointsInfo
,MinScore
,MaxScore
]
= displaypoints(___,Name,Value
)
Examples
Display Unscaled Points
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: logit(status) ~ 1 + CustAge + ResStatus + EmpStatus + CustIncome + TmWBank + OtherCC + AMBalance 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=37×3 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
{'CustAge' } {'<missing>' } NaN
{'ResStatus' } {'Tenant' } -0.031252
{'ResStatus' } {'Home Owner' } 0.12696
{'ResStatus' } {'Other' } 0.37641
{'ResStatus' } {'<missing>' } NaN
{'EmpStatus' } {'Unknown' } -0.076317
{'EmpStatus' } {'Employed' } 0.31449
{'EmpStatus' } {'<missing>' } NaN
{'CustIncome'} {'[-Inf,29000)'} -0.45716
⋮
displaypoints
always displays a '<missing>'
bin for each predictor. The value of the '<missing>'
bin comes from the initial creditscorecard
object, and the '<missing>'
bin is set to NaN
whenever the scorecard model has no information on how to assign points to missing data.
To configure the points for the '<missing>'
bin, you must use the initial creditscorecard
object. For predictors that have missing values in the training set, the points for the '<missing>'
bin are estimated from the data if the 'BinMissingData'
name-value pair argument is set to true
using creditscorecard
. When the 'BinMissingData'
parameter is set to false
, or when the data contains no missing values in the training set, use the 'Missing'
name-value pair argument in formatpoints
to indicate how to assign points to the missing data.
Display Unscaled Points When Using Missing Data
Create a creditscorecard
object using the CreditCardData.mat
file to load the data
with missing values.
load CreditCardData.mat
head(dataMissing,5)
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: {'CustID' 'CustAge' 'TmAtAddress' 'ResStatus' 'EmpStatus' 'CustIncome' 'TmWBank' 'OtherCC' 'AMBalance' 'UtilRate' 'status'} NumericPredictors: {'CustAge' 'TmAtAddress' 'CustIncome' 'TmWBank' 'AMBalance' 'UtilRate'} CategoricalPredictors: {'ResStatus' 'EmpStatus' 'OtherCC'} BinMissingData: 1 IDVar: 'CustID' PredictorVars: {'CustAge' 'TmAtAddress' 'ResStatus' 'EmpStatus' 'CustIncome' 'TmWBank' 'OtherCC' 'AMBalance' 'UtilRate'} 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: logit(status) ~ 1 + CustAge + ResStatus + EmpStatus + CustIncome + TmWBank + OtherCC + AMBalance 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=38×3 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
{'EmpStatus'} {'<missing>' } NaN
⋮
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.
Display Scaled Points
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: logit(status) ~ 1 + CustAge + ResStatus + EmpStatus + CustIncome + TmWBank + OtherCC + AMBalance 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=37×3 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
{'CustAge' } {'<missing>' } NaN
{'ResStatus' } {'Tenant' } 62.421
{'ResStatus' } {'Home Owner' } 82.276
{'ResStatus' } {'Other' } 113.58
{'ResStatus' } {'<missing>' } NaN
{'EmpStatus' } {'Unknown' } 56.765
{'EmpStatus' } {'Employed' } 105.81
{'EmpStatus' } {'<missing>' } NaN
{'CustIncome'} {'[-Inf,29000)'} 8.9706
⋮
MinScore = 300
MaxScore = 850
Notice that, as expected, the values of MinScore
and MaxScore
correspond to the worst and best possible scores.
Separate the Base Points From the Total Points
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: logit(status) ~ 1 + CustAge + ResStatus + EmpStatus + CustIncome + TmWBank + OtherCC + AMBalance 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=38×3 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
{'CustAge' } {'<missing>' } NaN
{'ResStatus' } {'Tenant' } -0.13159
{'ResStatus' } {'Home Owner'} 0.026616
{'ResStatus' } {'Other' } 0.27607
{'ResStatus' } {'<missing>' } NaN
{'EmpStatus' } {'Unknown' } -0.17666
{'EmpStatus' } {'Employed' } 0.21415
{'EmpStatus' } {'<missing>' } NaN
⋮
Display Points After Modifying Bin Labels
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: logit(status) ~ 1 + CustAge + ResStatus + EmpStatus + CustIncome + TmWBank + OtherCC + AMBalance 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=37×3 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
{'CustAge' } {'<missing>' } NaN
{'ResStatus' } {'Tenant' } -0.031252
{'ResStatus' } {'Home Owner' } 0.12696
{'ResStatus' } {'Other' } 0.37641
{'ResStatus' } {'<missing>' } NaN
{'EmpStatus' } {'Unknown' } -0.076317
{'EmpStatus' } {'Employed' } 0.31449
{'EmpStatus' } {'<missing>' } NaN
{'CustIncome'} {'[-Inf,29000)'} -0.45716
⋮
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=37×3 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
{'CustAge' } {'<missing>' } NaN
{'ResStatus' } {'Tenant' } -0.031252
{'ResStatus' } {'Home Owner' } 0.12696
{'ResStatus' } {'Other' } 0.37641
{'ResStatus' } {'<missing>' } NaN
{'EmpStatus' } {'Unknown' } -0.076317
{'EmpStatus' } {'Employed' } 0.31449
{'EmpStatus' } {'<missing>' } NaN
{'CustIncome'} {'[-Inf,29000)'} -0.45716
⋮
MinScore = -1.3100
MaxScore = 3.0726
Compute the Predictor Weights
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: logit(status) ~ 1 + CustAge + ResStatus + EmpStatus + CustIncome + TmWBank + OtherCC + AMBalance 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 {'CustAge' } {'<missing>' } NaN {'ResStatus'} {'Tenant' } 62.031 {'ResStatus'} {'Home Owner'} 73.444
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.
Display Points for creditscorecard
Object That Contains Missing Data
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
(Risk Management Toolbox) 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
(Risk Management Toolbox) 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=51×3 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
{'CustID' } {'<missing>' } 12.085
{'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
⋮
MinScore = 300.0000
MaxScore = 800.0000
Input Arguments
sc
— Credit scorecard model
creditscorecard
object
Credit scorecard model, specified as a
creditscorecard
object. Use creditscorecard
to create
a creditscorecard
object.
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
Example: [PointsInfo,MinScore,MaxScore] =
displaypoints(sc,‘ShowCategoricalMembers’,true)
ShowCategoricalMembers
— Indicator for how to display bins labels of categories that were grouped together
false
(default) | true
or false
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
,…,Group
n,
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
PointsInfo
— One row per bin, per predictor, with the corresponding points
table
One row per bin, per predictor, with the corresponding points, returned as a table. For example:
Predictors | Bin | Points |
---|---|---|
Predictor_1 | Bin_11 | Points_11 |
Predictor_1 | Bin_12 | Points_12 |
Predictor_1 | Bin_13 | Points_13 |
... | ... | |
Predictor_1 | '<missing>' | NaN (Default) |
Predictor_2 | Bin_21 | Points_21 |
Predictor_2 | Bin_22 | Points_22 |
Predictor_2 | Bin_23 | Points_23 |
... | ... | |
Predictor_2 | '<missing>' | NaN (Default) |
Predictor_j | Bin_ji | Points_ji |
... | ... | |
Predictor_j | '<missing>' | NaN (Default) |
displaypoints
always displays a
'<missing>'
bin for each predictor. The value of
the '<missing>'
bin comes from the initial creditscorecard
object, and
the '<missing>'
bin is set to NaN
whenever the scorecard model has no information on how to assign points to
missing data.
To configure the points for the '<missing>'
bin, you
must use the initial creditscorecard
object. For
predictors that have missing values in the training set, the points for the
'<missing>'
bin are estimated from the data if the
'BinMissingData'
name-value pair argument for is set
to true
using creditscorecard
. When the
'BinMissingData'
parameter is set to
false
, or when the data contains no missing values in
the training set, use the 'Missing'
name-value pair
argument in formatpoints
to indicate
how to assign points to the missing data.
Another option is to use fillmissing
to
specify replacement "fill" values for predictors with a
NaN
or <undefined>
value. If
you use fillmissing
,
then the displaypoints
'<missing>'
row has the same points as the bin
associated with the fill value.
When base points are reported separately (see formatpoints
), the first
row of the returned PointsInfo
table contains the base
points.
MinScore
— Minimum possible total score
scalar
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.
MaxScore
— Maximum possible total score
scalar
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))
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,
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.
Version History
Introduced in R2014b
See Also
creditscorecard
| autobinning
| bininfo
| predictorinfo
| modifypredictor
| plotbins
| fillmissing
| modifybins
| bindata
| fitmodel
| formatpoints
| score
| setmodel
| probdefault
| validatemodel
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