modifypredictor
Set properties of credit scorecard predictors
Description
sets the properties of the credit scorecard predictors.sc
= modifypredictor(sc
,PredictorName
)
sets the properties of the credit scorecard predictors using optional name-value
pair arguments.sc
= modifypredictor(___,Name,Value
)
Examples
Modify a Predictor to Change the Predictor Type from Numeric to Categorical
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: [1200x11 table]
[T,Stats] = predictorinfo(sc,'ResStatus')
T=1×4 table
PredictorType LatestBinning LatestFillMissingType LatestFillMissingValue
_____________ _________________ _____________________ ______________________
ResStatus {'Numeric'} {'Original Data'} {'Original'} {0x0 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: [1200x11 table]
[T,Stats] = predictorinfo(sc,'ResStatus')
T=1×5 table
PredictorType Ordinal LatestBinning LatestFillMissingType LatestFillMissingValue
_______________ _______ _________________ _____________________ ______________________
ResStatus {'Categorical'} false {'Original Data'} {'Original'} {0x0 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
sc
— Credit scorecard model
creditscorecard
object
Credit scorecard model, specified as a
creditscorecard
object. Use creditscorecard
to create
a creditscorecard
object.
PredictorName
— Predictor name
character vector | cell array of character vectors
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
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)
PredictorType
— Predictor type that one or more predictors are converted to
''
no conversion occurs (default) | character vector with values 'Numeric'
,
'Categorical'
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 byPredictorName
is converted to numeric.'Categorical'
— The predictor data specified byPredictorName
is converted to categorical.
Data Types: char
Ordinal
— Indicator for whether predictors being converted to categorical are ordinal
false
(default) | logical with values true
,
false
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
sc
— Credit scorecard model
creditscorecard
object
Credit scorecard model, returned as an updated
creditscorecard
object.
Version History
Introduced in R2015b
See Also
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