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Summary of credit scorecard predictor properties


[T,Stats] = predictorinfo(sc,PredictorName)



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

Obtain the predictor statistics for the PredictorName of CustAge.

[T,Stats] = predictorinfo(sc,'CustAge')
T=1x2 table
               PredictorType     LatestBinning 
               _____________    _______________

    CustAge    'Numeric'        'Original Data'

Stats=4x1 table

    Min         21
    Max         74
    Mean    45.174
    Std     9.8343

Obtain the predictor statistics for the PredictorName of ResStatus.

[T,Stats] = predictorinfo(sc,'ResStatus')
T=1x3 table
                 PredictorType    Ordinal     LatestBinning 
                 _____________    _______    _______________

    ResStatus    'Categorical'    false      'Original Data'

Stats=3x1 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

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