modifypredictor

Set properties of credit scorecard predictors

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Syntax

sc = modifypredictor(sc,PredictorName)
sc = modifypredictor(___,Name,Value)

Description

sc = modifypredictor(sc,PredictorName) sets the properties of the credit scorecard predictors.

example

sc = modifypredictor(___,Name,Value) sets the properties of the credit scorecard predictors using optional name-value pair arguments.

example

Examples

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Open Live Script

Create a creditscorecard object using the CreditCardData.mat file to load the data (using a dataset from Refaat 2011). In practice, categorical data many times is represented with numeric values. To show the case where categorical data is given as numeric data, the data for the variable 'ResStatus' is intentionally converted to numeric values. 

load CreditCardData
data.ResStatus = double(data.ResStatus);
sc = creditscorecard(data,'IDVar','CustID')
sc = 
  creditscorecard with properties:

                GoodLabel: 0
              ResponseVar: 'status'
               WeightsVar: ''
                 VarNames: {'CustID'  'CustAge'  'TmAtAddress'  'ResStatus'  'EmpStatus'  'CustIncome'  'TmWBank'  'OtherCC'  'AMBalance'  'UtilRate'  'status'}
        NumericPredictors: {'CustAge'  'TmAtAddress'  'ResStatus'  'CustIncome'  'TmWBank'  'AMBalance'  'UtilRate'}
    CategoricalPredictors: {'EmpStatus'  'OtherCC'}
           BinMissingData: 0
                    IDVar: 'CustID'
            PredictorVars: {'CustAge'  'TmAtAddress'  'ResStatus'  'EmpStatus'  'CustIncome'  'TmWBank'  'OtherCC'  'AMBalance'  'UtilRate'}
                     Data: [1200×11 table]


[T,Stats] = predictorinfo(sc,'ResStatus')
T=1×4 table
                 PredictorType      LatestBinning      LatestFillMissingType    LatestFillMissingValue
                 _____________    _________________    _____________________    ______________________

    ResStatus     {'Numeric'}     {'Original Data'}        {'Original'}              {0×0 double}     


Stats=4×1 table
             Value 
            _______

    Min           1
    Max           3
    Mean     1.7017
    Std     0.71833

Note that 'ResStatus' appears as part of the NumericPredictors property. Assume that you want 'ResStatus' to be treated as categorical data. For example, you may want to allow automatic binning algorithms to reorder the categories. Use modifypredictor to change the 'PredictorType' of the PredictorName 'ResStatus' from numeric to categorical. 

sc = modifypredictor(sc,'ResStatus','PredictorType','Categorical')
sc = 
  creditscorecard with properties:

                GoodLabel: 0
              ResponseVar: 'status'
               WeightsVar: ''
                 VarNames: {'CustID'  'CustAge'  'TmAtAddress'  'ResStatus'  'EmpStatus'  'CustIncome'  'TmWBank'  'OtherCC'  'AMBalance'  'UtilRate'  'status'}
        NumericPredictors: {'CustAge'  'TmAtAddress'  'CustIncome'  'TmWBank'  'AMBalance'  'UtilRate'}
    CategoricalPredictors: {'ResStatus'  'EmpStatus'  'OtherCC'}
           BinMissingData: 0
                    IDVar: 'CustID'
            PredictorVars: {'CustAge'  'TmAtAddress'  'ResStatus'  'EmpStatus'  'CustIncome'  'TmWBank'  'OtherCC'  'AMBalance'  'UtilRate'}
                     Data: [1200×11 table]


[T,Stats] = predictorinfo(sc,'ResStatus')
T=1×5 table
                  PredictorType     Ordinal      LatestBinning      LatestFillMissingType    LatestFillMissingValue
                 _______________    _______    _________________    _____________________    ______________________

    ResStatus    {'Categorical'}     false     {'Original Data'}        {'Original'}              {0×0 double}     


Stats=3×1 table
          Count
          _____

    C1     542 
    C2     474 
    C3     184

Notice that 'ResStatus' now appears as part of the 'Categorical' predictors.

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 or cell array of character vectors containing the names of the credit scorecard predictors. PredictorName is case-sensitive.

Data Types: char | cell

Name-Value Arguments

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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: sc = modifypredictor(sc,{'CustAge','CustIncome'},'PredictorType','Categorical','Ordinal',true)

Predictor type that one or more predictors are converted to, specified as the comma-separated pair consisting of 'PredictorType' and a character vector. Possible values are:

  • '' — No conversion occurs.

  • 'Numeric' — The predictor data specified by PredictorName is converted to numeric.

  • 'Categorical' — The predictor data specified by PredictorName is converted to categorical.

Data Types: char

Indicator for whether predictors being converted to categorical or existing categorical predictors are treated as ordinal data, specified as the comma-separated pair consisting of 'Ordinal' and a logical with values true or false.

Note

This optional input parameter is only used for predictors of type 'Categorical'.

Data Types: logical

Output Arguments

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Credit scorecard model, returned as an updated creditscorecard object.

Version History

Introduced in R2015b

See Also

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