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probdefault

Likelihood of default for given data set

Syntax

pd = probdefault(sc)
pd = probdefault(sc,data)

Description

example

pd = probdefault(sc) computes the probability of default for sc, the data used to build the creditscorecard object.

example

pd = probdefault(sc,data) computes the probability of default for a given data set specified using the optional argument data.

By default, the data used to build the creditscorecard object are used. You can also supply input data, to which the same computation of probability of default is applied.

Examples

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Create a creditscorecard object using the CreditCardData.mat file to load the data (using a dataset from Refaat 2011).

load CreditCardData
sc = creditscorecard(data,'IDVar','CustID')
sc = 
  creditscorecard with properties:

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

Perform automatic binning using the default options. By default, autobinning uses the Monotone algorithm.

sc = autobinning(sc);

Fit the model.

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 the probability of default.

pd = probdefault(sc);
disp(pd(1:15,:))
    0.2503
    0.1878
    0.3173
    0.1711
    0.1895
    0.1307
    0.5218
    0.2848
    0.2612
    0.3047
    0.3418
    0.2237
    0.2793
    0.3615
    0.1653

Input Arguments

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Credit scorecard model, specified as a creditscorecard object. To create this object, use creditscorecard.

(Optional) Dataset to apply probability of default rules, specified as a MATLAB® table, where each row corresponds to individual observations. The data must contain columns for each of the predictors in the creditscorecard object.

Data Types: table

Output Arguments

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Probability of default, returned as a NumObs-by-1 numerical array of default probabilities.

More About

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Default Probability

After the unscaled scores are computed (see Algorithms for Computing and Scaling Scores), the probability of the points being “Good” is represented by the following formula:

ProbGood = 1./(1 + exp(-UnscaledScores))

Thus, the probability of default is

pd = 1 - ProbGood

References

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

Introduced in R2015a

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