plotbins

Plot histogram counts for predictor variables

collapse all in page

Syntax

plotbins(sc,PredictorName)
hFigure = plotbins(sc,PredictorName)
hFigure = plotbins(___,Name,Value)

Description

example

plotbins(sc,PredictorName) plots histogram counts for given predictor variables. When a predictor’s bins are modified using modifybins or autobinning, rerun plotbins to update the figure to reflect the change.

example

hFigure = plotbins(sc,PredictorName) returns a handle to the figure. plotbins plots histogram counts for given predictor variables. When a predictor’s bins are modified using modifybins or autobinning, rerun plotbins to update the figure to reflect the change.

example

hFigure = plotbins(___,Name,Value) returns a handle to the figure. plotbins plots histogram counts for given predictor variables using optional name-value pair arguments. When a predictor’s bins are modified using modifybins or autobinning, rerun plotbins to update the figure to reflect the change.

Examples

collapse all

Open Live Script

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

load CreditCardData 
sc = creditscorecard(data);

Perform automatic binning for the PredictorName input argument for CustIncome using the defaults for the algorithm Monotone. 

sc = autobinning(sc, 'CustIncome')
sc = 
  creditscorecard with properties:

                GoodLabel: 0
              ResponseVar: 'status'
               WeightsVar: ''
                 VarNames: {1x11 cell}
        NumericPredictors: {1x7 cell}
    CategoricalPredictors: {'ResStatus'  'EmpStatus'  'OtherCC'}
           BinMissingData: 0
                    IDVar: ''
            PredictorVars: {1x10 cell}
                     Data: [1200x11 table]

Use bininfo to display the autobinned data. 

[bi, cp] = bininfo(sc, 'CustIncome')
bi=8×6 table
           Bin           Good    Bad     Odds         WOE       InfoValue 
    _________________    ____    ___    _______    _________    __________

    {'[-Inf,29000)' }     53      58    0.91379     -0.79457       0.06364
    {'[29000,33000)'}     74      49     1.5102     -0.29217     0.0091366
    {'[33000,35000)'}     68      36     1.8889     -0.06843    0.00041042
    {'[35000,40000)'}    193      98     1.9694    -0.026696    0.00017359
    {'[40000,42000)'}     68      34          2    -0.011271    1.0819e-05
    {'[42000,47000)'}    164      66     2.4848      0.20579     0.0078175
    {'[47000,Inf]'  }    183      56     3.2679      0.47972      0.041657
    {'Totals'       }    803     397     2.0227          NaN       0.12285


cp = 6×1

       29000
       33000
       35000
       40000
       42000
       47000

Manually remove the second cut point (the boundary between the second and third bins) to merge bins two and three. Use the modifybins function to update the scorecard and then display updated bin information. 

cp(2) = [];
sc = modifybins(sc,'CustIncome','CutPoints',cp);
bi = bininfo(sc,'CustIncome')
bi=7×6 table
           Bin           Good    Bad     Odds         WOE       InfoValue 
    _________________    ____    ___    _______    _________    __________

    {'[-Inf,29000)' }     53      58    0.91379     -0.79457       0.06364
    {'[29000,35000)'}    142      85     1.6706     -0.19124     0.0071274
    {'[35000,40000)'}    193      98     1.9694    -0.026696    0.00017359
    {'[40000,42000)'}     68      34          2    -0.011271    1.0819e-05
    {'[42000,47000)'}    164      66     2.4848      0.20579     0.0078175
    {'[47000,Inf]'  }    183      56     3.2679      0.47972      0.041657
    {'Totals'       }    803     397     2.0227          NaN       0.12043

Plot the histogram count for updated bin information for the PredictorName called CustIncome. 

plotbins(sc,'CustIncome')
xtickangle(30)
legend('Location','north')

Open Live Script

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

load CreditCardData 
sc = creditscorecard(data);

Perform automatic binning for the PredictorName input argument for CustIncome using the defaults for the algorithm Monotone. 

sc = autobinning(sc, 'CustIncome')
sc = 
  creditscorecard with properties:

                GoodLabel: 0
              ResponseVar: 'status'
               WeightsVar: ''
                 VarNames: {1x11 cell}
        NumericPredictors: {1x7 cell}
    CategoricalPredictors: {'ResStatus'  'EmpStatus'  'OtherCC'}
           BinMissingData: 0
                    IDVar: ''
            PredictorVars: {1x10 cell}
                     Data: [1200x11 table]

Use bininfo to display the autobinned data. 

[bi, cp] = bininfo(sc, 'CustIncome')
bi=8×6 table
           Bin           Good    Bad     Odds         WOE       InfoValue 
    _________________    ____    ___    _______    _________    __________

    {'[-Inf,29000)' }     53      58    0.91379     -0.79457       0.06364
    {'[29000,33000)'}     74      49     1.5102     -0.29217     0.0091366
    {'[33000,35000)'}     68      36     1.8889     -0.06843    0.00041042
    {'[35000,40000)'}    193      98     1.9694    -0.026696    0.00017359
    {'[40000,42000)'}     68      34          2    -0.011271    1.0819e-05
    {'[42000,47000)'}    164      66     2.4848      0.20579     0.0078175
    {'[47000,Inf]'  }    183      56     3.2679      0.47972      0.041657
    {'Totals'       }    803     397     2.0227          NaN       0.12285


cp = 6×1

       29000
       33000
       35000
       40000
       42000
       47000

Plot the bin information for CustIncome without the Weight of Evidence (WOE) line and without a legend by setting the 'WOE' and 'Legend' name-value arguments to 'Off'. Also, set the 'BinText' name-value pair argument to 'PercentRows' to show as text over the plot bars for the proportion of "Good" and "Bad" within each bin, that is, the probability of "Good" and "Bad" within each bin. 

plotbins(sc,'CustIncome','WOE','Off','Legend','Off','BinText','PercentRows')
xtickangle(30)

Open Live Script

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. 

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')

Input Arguments

collapse all

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

Name of one or more predictors to plot, specified using a character vector or cell array of character vectors containing one or more names of the predictors.

Data Types: char | cell

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: plotbins(sc,PredictorName,'BinText','Count','WOE','On')

Information to display on top of plotted bin counts, specified as the comma-separated pair consisting of 'BinText' and a character vector with values:

  • None — No text is displayed on top of the bins.

  • Count — For each bin, displays the count for “Good” and “Bad.”

  • PercentRows — For each bin, displays the count for “Good” and “Bad” as a percentage of the number of observations in the bin.

  • PercentCols — For each bin, displays the count for “Good” and “Bad” as a percentage of the total “Good” and total “Bad” in the entire sample.

  • PercentTotal — For each bin, displays the count for “Good” and “Bad” as a percentage of the total number of observations in the entire sample.

Data Types: char

Indicator for Weight of Evidence (WOE) line, specified as the comma-separated pair consisting of 'WOE' and a character vector with values On or Off. When set to On, the WOE line is plotted on top of the histogram.

Data Types: char

Indicator for legend on the plot, specified as the comma-separated pair consisting of 'Legend' and a character vector with values On or Off.

Data Types: char

Output Arguments

collapse all

Figure handle for histogram plot for predictor variables, returned as figure object or array of figure objects if more than one PredictorName is specified as an input.

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.

See Also

| | | | | | | | | | | | |

Topics

Introduced in R2014b

Financial Toolbox Documentation
Support
A Practical Guide to Modeling Financial Risk with MATLAB

A Practical Guide to Modeling Financial Risk with MATLAB

Download ebook