Summary of credit scorecard predictor properties



[T,Stats] = predictorinfo(sc,PredictorName) returns a summary of credit scorecard predictor properties and some basic predictor statistics.


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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'}
           BinMissingData: 0
                    IDVar: 'CustID'
            PredictorVars: {1x9 cell}
                     Data: [1200x11 table]

Obtain the predictor statistics for the PredictorName of CustAge.

[T,Stats] = predictorinfo(sc,'CustAge')
T=1×2 table
               PredictorType      LatestBinning  
               _____________    _________________

    CustAge     {'Numeric'}     {'Original Data'}

Stats=4×1 table

    Min         21
    Max         74
    Mean    45.174
    Std     9.8302

Obtain the predictor statistics for the PredictorName of ResStatus.

[T,Stats] = predictorinfo(sc,'ResStatus')
T=1×3 table
                  PredictorType     Ordinal      LatestBinning  
                 _______________    _______    _________________

    ResStatus    {'Categorical'}     false     {'Original Data'}

Stats=3×1 table

    Home Owner     542 
    Tenant         474 
    Other          184 

Input Arguments

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

Predictor name, specified using a character vector containing the names of the credit scorecard predictor of interest. PredictorName is case-sensitive.

Data Types: char

Output Arguments

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Summary information for specified predictor, returned as table with the following columns:

  • 'PredictorType''Numeric' or 'Categorical'.

  • 'Ordinal' — For categorical predictors, a boolean indicating whether it is ordinal.

  • 'LatestBinning' — Character vector indicating the last applied algorithm for the input argument PredictorName. The values are:

    • 'Original Data' — When no binning is applied to the predictor.

    • 'Automatic / BinningName' — Where 'BinningName' is one of the following: Monotone, Equal Width, or Equal Frequency.

    • 'Manual' — After each call of modifybins, where either 'CutPoints', 'CatGrouping', 'MinValue', or 'MaxValue' are modified.

The predictor’s name is used as a row name in the table that is returned.

Summary statistics for the input PredictorName, returned as a table. The corresponding value is stored in the 'Value' column.

The table’s row names indicate the relevant statistics for numeric predictors:

  • 'Min' — Minimum value in the sample.

  • 'Max' — Maximum value in the sample.

  • 'Mean' — Mean value in the sample.

  • 'Std' — Standard deviation of the sample.


    For data types other than 'double' or 'single', numeric precision may be lost for the standard deviation. Data types other than 'double' or 'single' are cast as 'double' before computing the standard deviation.

For categorical predictors, the row names contain the names of the categories, with corresponding total count in the 'Count' column.

Introduced in R2015b