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modelDiscriminationPlot

Plot ROC curve

Since R2021a

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Syntax

modelDiscriminationPlot(pdModel,data)
modelDiscriminationPlot(___,Name,Value)
h = modelDiscriminationPlot(ax,___,Name,Value)

Description

modelDiscriminationPlot(pdModel,data) plots the receiver operating characteristic curve (ROC). modelDiscriminationPlot supports segmentation and comparison against a reference model.

example

modelDiscriminationPlot(___,Name,Value) specifies options using one or more name-value pair arguments in addition to the input arguments in the previous syntax.

example

h = modelDiscriminationPlot(ax,___,Name,Value) specifies options using one or more name-value pair arguments in addition to the input arguments in the previous syntax and returns the figure handle h.

example

Examples

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Open Live Script

This example shows how to use modelDiscriminationPlot to plot the ROC curve.

Load Data

Load the credit portfolio data. 

load RetailCreditPanelData.mat
disp(head(data))
    ID    ScoreGroup    YOB    Default    Year
    __    __________    ___    _______    ____

    1      Low Risk      1        0       1997
    1      Low Risk      2        0       1998
    1      Low Risk      3        0       1999
    1      Low Risk      4        0       2000
    1      Low Risk      5        0       2001
    1      Low Risk      6        0       2002
    1      Low Risk      7        0       2003
    1      Low Risk      8        0       2004

disp(head(dataMacro))
    Year     GDP     Market
    ____    _____    ______

    1997     2.72      7.61
    1998     3.57     26.24
    1999     2.86      18.1
    2000     2.43      3.19
    2001     1.26    -10.51
    2002    -0.59    -22.95
    2003     0.63      2.78
    2004     1.85      9.48

Join the two data components into a single data set.

data = join(data,dataMacro);
disp(head(data))
    ID    ScoreGroup    YOB    Default    Year     GDP     Market
    __    __________    ___    _______    ____    _____    ______

    1      Low Risk      1        0       1997     2.72      7.61
    1      Low Risk      2        0       1998     3.57     26.24
    1      Low Risk      3        0       1999     2.86      18.1
    1      Low Risk      4        0       2000     2.43      3.19
    1      Low Risk      5        0       2001     1.26    -10.51
    1      Low Risk      6        0       2002    -0.59    -22.95
    1      Low Risk      7        0       2003     0.63      2.78
    1      Low Risk      8        0       2004     1.85      9.48

Partition Data

Separate the data into training and test partitions. 

nIDs = max(data.ID);
uniqueIDs = unique(data.ID);

rng('default'); % For reproducibility
c = cvpartition(nIDs,'HoldOut',0.4);

TrainIDInd = training(c);
TestIDInd = test(c);

TrainDataInd = ismember(data.ID,uniqueIDs(TrainIDInd));
TestDataInd = ismember(data.ID,uniqueIDs(TestIDInd));

Create Logistic Lifetime PD Model

Use fitLifetimePDModel to create a Logistic model using the training data. 

pdModel = fitLifetimePDModel(data(TrainDataInd,:),'logistic',...
        'ModelID','Example',...
        'Description','Lifetime PD model using RetailCreditPanelData.',...
        'IDVar','ID',...
        'AgeVar','YOB',...
        'LoanVars','ScoreGroup',...
        'MacroVars',{'GDP' 'Market'},...
        'ResponseVar','Default');
 disp(pdModel)
  Logistic with properties:

            ModelID: "Example"
        Description: "Lifetime PD model using RetailCreditPanelData."
    UnderlyingModel: [1x1 classreg.regr.CompactGeneralizedLinearModel]
              IDVar: "ID"
             AgeVar: "YOB"
           LoanVars: "ScoreGroup"
          MacroVars: ["GDP"    "Market"]
        ResponseVar: "Default"
         WeightsVar: ""
       TimeInterval: 1

disp(pdModel.UnderlyingModel)
Compact generalized linear regression model:
    logit(Default) ~ 1 + ScoreGroup + YOB + GDP + Market
    Distribution = Binomial

Estimated Coefficients:
                               Estimate        SE         tStat       pValue   
                              __________    _________    _______    ___________

    (Intercept)                  -2.7422      0.10136    -27.054     3.408e-161
    ScoreGroup_Medium Risk      -0.68968     0.037286    -18.497     2.1894e-76
    ScoreGroup_Low Risk          -1.2587     0.045451    -27.693    8.4736e-169
    YOB                         -0.30894     0.013587    -22.738    1.8738e-114
    GDP                         -0.11111     0.039673    -2.8006      0.0051008
    Market                    -0.0083659    0.0028358    -2.9502      0.0031761


388097 observations, 388091 error degrees of freedom
Dispersion: 1
Chi^2-statistic vs. constant model: 1.85e+03, p-value = 0

Visualize Model Discrimination

Use modelDiscriminationPlot to plot the ROC for the test data.

modelDiscriminationPlot(pdModel,data(TestDataInd,:))

Figure contains an axes object. The axes object with title ROC Example, AUROC = 0.70009, xlabel Fraction of Non-Defaulters, ylabel Fraction of Defaulters contains an object of type line. This object represents Example.

Input Arguments

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Probability of default model, specified as a Logistic, Probit, or Cox object previously created using fitLifetimePDModel. Alternatively, you can create a custom probability of default model using customLifetimePDModel.

Note

The 'ModelID' property of the pdModel object is used as the identifier or tag for pdModel.

Data Types: object

Data, specified as a NumRows-by-NumCols table with projected predictor values to make lifetime predictions. The predictor names and data types must be consistent with the underlying model.

Data Types: table

(Optional) Valid axis object, specified as an ax object that is created using axes. The plot will be created in the axes specified by the optional ax argument instead of in the current axes (gca). The optional argument ax must precede any of the input argument combinations.

Data Types: 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: modelDiscriminationPlot(pdModel,data(Ind,:),'DataID',"DataSetChoice")

Data set identifier, specified as the comma-separated pair consisting of 'DataID' and a character vector or string. The DataID is included in the plot title for reporting purposes.

Data Types: char | string

Name of a column in the data input, not necessarily a model variable, to be used to segment the data set, specified as the comma-separated pair consisting of 'SegmentBy' and a character vector or string. modelDiscriminationPlot plots one ROC for each segment.

Data Types: char | string

Conditional PD values predicted for data by the reference model, specified as the comma-separated pair consisting of 'ReferencePD' and a NumRows-by-1 numeric vector. The ROC curve output information is plotted for both the pdModel object and the reference model.

Data Types: double

Identifier for the reference model, specified as the comma-separated pair consisting of 'ReferenceID' and a character vector or string. 'ReferenceID' is used in the plot for reporting purposes.

Data Types: char | string

Output Arguments

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Figure handle for the line objects, returned as handle object.

More About

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Model Discrimination

Model discrimination measures the risk ranking.

Higher-risk loans should get higher predicted probability of default (PD) than lower-risk loans. The modelDiscrimination function computes the area under the receiver operator characteristic curve (AUROC), sometimes called simply the area under the curve (AUC). This metric is between 0 and 1 and higher values indicate better discrimination.

The receiver operator characteristic (ROC) curve is a parametric curve that plots the proportion of

  • Defaulters with PD higher than or equal to a reference PD value p

  • Nondefaulters with PD higher than or equal to the same reference PD value p

The reference PD value p parameterizes the curve, and the software sweeps through the unique predicted PD values observed in a data set. The proportion of actual defaulters are assigned a PD higher than or equal to p is the true positive rate. The proportion of actual nondefaulters that are assigned a PD higher than or equal to p is the false positive rate." For more information about ROC curves, see ROC Curve and Performance Metrics.

The AUROC is reported on the plot created by modelDiscriminationPlot. To get the AUROC metric programmatically, use modelDiscrimination.

References

[1] Baesens, Bart, Daniel Roesch, and Harald Scheule. Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS. Wiley, 2016.

[2] Bellini, Tiziano. IFRS 9 and CECL Credit Risk Modelling and Validation: A Practical Guide with Examples Worked in R and SAS. San Diego, CA: Elsevier, 2019.

[3] Breeden, Joseph. Living with CECL: The Modeling Dictionary. Santa Fe, NM: Prescient Models LLC, 2018.

[4] Roesch, Daniel and Harald Scheule. Deep Credit Risk: Machine Learning with Python. Independently published, 2020.

Version History

Introduced in R2021a

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See Also

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