bindata
Binned predictor variables
Description
binned predictor variables returned as a table using optional name-value pair
arguments. This is a table of the same size as the data input, but only the
predictors specified in the bdata
= bindata(sc,Name,Value
)creditscorecard
object's
PredictorVars
property are binned and the remaining ones
are unchanged.
Examples
Bin creditscorecard
Data as Bin Numbers, Categories, or WOE Values
This example shows how to use the bindata
function to simply bin or discretize data.
Suppose bin ranges of
'0 to 30'
'31 to 50'
'51 and up'
are determined for the age variable (via manual or automatic binning). If a data point with age 41 is given, binning this data point means placing it in the bin for 41 years old, which is the second bin, or the '31 to 50' bin. Binning is then the mapping from the original data, into discrete groups or bins. In this example, you can say that a 41-year old is mapped into bin number 2, or that it is binned into the '31 to 50' category. If you know the Weight of Evidence (WOE) value for each of the three bins, you could also replace the data point 41 with the WOE value corresponding to the second bin. bindata
supports the three binning formats just mentioned:
Bin number (where the
'OutputType'
name-value pair argument is set to'BinNumber'
); this is the default option, and in this case, 41 is mapped to bin 2.Categorical (where the
'OutputType'
name-value pair argument is set to'Categorical'
); in this case, 41 is mapped to the '31 to 50' bin.WOE value (where the
'OutputType'
name-value pair argument is set to'WOE'
); in this case, 41 is mapped to the WOE value of bin number 2.
Create a creditscorecard
object using the CreditCardData.mat
file to load the data
(using a dataset from Refaat 2011). Use the 'IDVar'
argument to indicate that 'CustID'
contains ID information and should not be included as a predictor variable.
load CreditCardData 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' '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]
Perform automatic binning.
sc = autobinning(sc);
Show the bin information for 'CustAge'
.
bininfo(sc,'CustAge')
ans=8×6 table
Bin Good Bad Odds WOE InfoValue
_____________ ____ ___ ______ _________ _________
{'[-Inf,33)'} 70 53 1.3208 -0.42622 0.019746
{'[33,37)' } 64 47 1.3617 -0.39568 0.015308
{'[37,40)' } 73 47 1.5532 -0.26411 0.0072573
{'[40,46)' } 174 94 1.8511 -0.088658 0.001781
{'[46,48)' } 61 25 2.44 0.18758 0.0024372
{'[48,58)' } 263 105 2.5048 0.21378 0.013476
{'[58,Inf]' } 98 26 3.7692 0.62245 0.0352
{'Totals' } 803 397 2.0227 NaN 0.095205
These are the first 10 age values in the original data, used to create the creditscorecard
object.
data(1:10,'CustAge')
ans=10×1 table
CustAge
_______
53
61
47
50
68
65
34
50
50
49
Bin scorecard data into bin numbers (default behavior).
bdata = bindata(sc);
According to the bin information, the first age should be mapped into the fourth bin, the second age into the fifth bin, etc. These are the first 10 binned ages, in bin-number format.
bdata(1:10,'CustAge')
ans=10×1 table
CustAge
_______
6
7
5
6
7
7
2
6
6
6
Bin the scorecard data and show their bin labels. To do this, set the bindata
name-value pair argument for 'OutputType'
to 'Categorical'
.
bdata = bindata(sc,'OutputType','Categorical');
These are the first 10 binned ages, in categorical format.
bdata(1:10,'CustAge')
ans=10×1 table
CustAge
________
[48,58)
[58,Inf]
[46,48)
[48,58)
[58,Inf]
[58,Inf]
[33,37)
[48,58)
[48,58)
[48,58)
Convert the scorecard data to WOE values. To do this, set the bindata
name-value pair argument for 'OutputType'
to 'WOE'
.
bdata = bindata(sc,'OutputType','WOE');
These are the first 10 binned ages, in WOE format. The ages are mapped to the WOE values that are internally displayed using the bininfo
function.
bdata(1:10,'CustAge')
ans=10×1 table
CustAge
________
0.21378
0.62245
0.18758
0.21378
0.62245
0.62245
-0.39568
0.21378
0.21378
0.21378
Bin Additional "Test" Data
This example shows how to use the bindata
function's optional input for the data to bin. If not provided, bindata
bins the creditscorecard
training data. However, if a different dataset needs to be binned, for example, some "test" data, this can be passed into bindata
as an optional input.
Create a creditscorecard
object using the CreditCardData.mat
file to load the data
(using a dataset from Refaat 2011). Use the 'IDVar'
argument to indicate that 'CustID'
contains ID information and should not be included as a predictor variable.
load CreditCardData 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' '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]
Perform automatic binning.
sc = autobinning(sc);
Show the bin information for 'CustAge'
.
bininfo(sc,'CustAge')
ans=8×6 table
Bin Good Bad Odds WOE InfoValue
_____________ ____ ___ ______ _________ _________
{'[-Inf,33)'} 70 53 1.3208 -0.42622 0.019746
{'[33,37)' } 64 47 1.3617 -0.39568 0.015308
{'[37,40)' } 73 47 1.5532 -0.26411 0.0072573
{'[40,46)' } 174 94 1.8511 -0.088658 0.001781
{'[46,48)' } 61 25 2.44 0.18758 0.0024372
{'[48,58)' } 263 105 2.5048 0.21378 0.013476
{'[58,Inf]' } 98 26 3.7692 0.62245 0.0352
{'Totals' } 803 397 2.0227 NaN 0.095205
For the purpose of illustration, take a few rows from the original data as "test" data and display the first 10 age values in the test data.
tdata = data(101:110,:);
tdata(1:10,'CustAge')
ans=10×1 table
CustAge
_______
34
59
64
61
28
65
55
37
49
51
Convert the test data to WOE values. To do this, set the bindata
name-value pair argument for 'OutputType'
to 'WOE'
, passing the test data (tdata
) as an optional input.
bdata = bindata(sc,tdata,'OutputType','WOE')
bdata=10×11 table
CustID CustAge TmAtAddress ResStatus EmpStatus CustIncome TmWBank OtherCC AMBalance UtilRate status
______ ________ ___________ _________ _________ __________ ________ ________ _________ ________ ______
101 -0.39568 -0.087767 -0.095564 0.2418 -0.011271 0.76889 0.053364 -0.11274 0.048576 0
102 0.62245 0.14288 0.019329 -0.19947 0.20579 -0.13107 -0.26832 -0.11274 0.048576 1
103 0.62245 0.02263 0.019329 0.2418 0.47972 -0.12109 0.053364 0.24418 0.092164 0
104 0.62245 0.02263 -0.095564 0.2418 0.47972 -0.12109 0.053364 0.24418 0.048576 0
105 -0.42622 0.02263 0.019329 0.2418 -0.06843 0.76889 0.053364 -0.11274 0.092164 0
106 0.62245 0.02263 0.019329 -0.19947 0.20579 -0.13107 0.053364 -0.11274 -0.22899 0
107 0.21378 -0.087767 -0.095564 0.2418 0.47972 0.26704 0.053364 -0.11274 0.048576 0
108 -0.26411 -0.087767 0.019329 -0.19947 -0.29217 -0.13107 0.053364 -0.11274 0.048576 0
109 0.21378 -0.087767 -0.095564 0.2418 -0.026696 -0.13107 0.053364 0.24418 0.048576 0
110 0.21378 -0.087767 0.019329 0.2418 0.20579 -0.13107 0.053364 -0.29895 -0.22899 0
These are the first 10 binned ages, in WOE format. The ages are mapped to the WOE values displayed internally by bininfo
.
bdata(1:10,'CustAge')
ans=10×1 table
CustAge
________
-0.39568
0.62245
0.62245
0.62245
-0.42622
0.62245
0.21378
-0.26411
0.21378
0.21378
Bin Additional "Test" Data When Using the 'BinMissingData'
Option
Create a creditscorecard
object using the CreditCardData.mat
file to load the data
with missing values. The variables CustAge
and ResStatus
have missing values.
load CreditCardData.mat
head(dataMissing,5)
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
Use creditscorecard
with the name-value argument 'BinMissingData'
set to true
to bin the missing numeric or categorical data in a separate bin. Apply automatic binning.
sc = creditscorecard(dataMissing,'IDVar','CustID','BinMissingData',true); sc = autobinning(sc); disp(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: 1 IDVar: 'CustID' PredictorVars: {'CustAge' 'TmAtAddress' 'ResStatus' 'EmpStatus' 'CustIncome' 'TmWBank' 'OtherCC' 'AMBalance' 'UtilRate'} 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')
For the 'CustAge'
and 'ResStatus'
predictors, there is missing data (NaNs and <undefined>
) in the training data, and the binning process estimates a WOE value of -0.15787
and 0.026469
respectively for missing data in these predictors, as shown above.
For the purpose of illustration, take a few rows from the original data as test data and introduce some missing data.
tdata = dataMissing(11:14,:); tdata.CustAge(1) = NaN; tdata.TmAtAddress(2) = NaN; tdata.ResStatus(3) = missing; tdata.EmpStatus(4) = missing; disp(tdata)
CustID CustAge TmAtAddress ResStatus EmpStatus CustIncome TmWBank OtherCC AMBalance UtilRate status ______ _______ ___________ ___________ ___________ __________ _______ _______ _________ ________ ______ 11 NaN 24 Tenant Unknown 34000 44 Yes 119.8 0.07 1 12 48 NaN Other Unknown 44000 14 Yes 403.62 0.03 0 13 65 63 <undefined> Unknown 48000 6 No 111.88 0.02 0 14 44 75 Other <undefined> 41000 35 No 436.41 0.18 0
Convert the test data to WOE values. To do this, set the bindata
name-value pair argument for 'OutputType'
to 'WOE'
, passing the test data tdata
as an optional input.
bdata = bindata(sc,tdata,'OutputType','WOE'); disp(bdata)
CustID CustAge TmAtAddress ResStatus EmpStatus CustIncome TmWBank OtherCC AMBalance UtilRate status ______ ________ ___________ _________ _________ __________ ________ ________ _________ ________ ______ 11 -0.15787 0.02263 -0.095463 -0.19947 -0.06843 -0.12109 0.053364 0.24418 0.048576 1 12 0.17713 NaN 0.19637 -0.19947 0.20579 -0.13107 0.053364 0.24418 0.092164 0 13 0.60931 0.02263 0.026469 -0.19947 0.47972 -0.25547 -0.26832 0.24418 0.092164 0 14 -0.04556 0.02263 0.19637 NaN -0.011271 -0.12109 -0.26832 0.24418 0.048576 0
For the 'CustAge'
and 'ResStatus'
predictors, because there is missing data in the training data, the missing values in the test data get mapped to the WOE value estimated for the <missing>
bin. Therefore, a missing value for 'CustAge'
is replaced with -0.15787
, and a missing value for 'ResStatus'
is replaced with 0.026469
.
For 'TmAtAddress'
and 'EmpStatus'
, the training data has no missing values, therefore there is no bin for missing data, and there is no way to estimate a WOE value for missing data. Therefore, for these predictors, the WOE transformation leaves missing values as missing (that is, sets a WOE value of NaN
).
These rules apply when 'OutputType'
is set to 'WOE'
or 'WOEModelInput'
. The rationale is that if a data-based WOE value exists for missing data, it should be used for the WOE transformation and for subsequent steps (for example, fitting a logistic model or scoring).
On the other hand, when 'OutputType'
is set to 'BinNumber'
or 'Categorical'
, bindata
leaves missing values as missing, since this allows you to subsequently treat the missing data as you see fit.
For example, when 'OutputType'
is set to 'BinNumber'
, missing values are set to NaN
:
bdata = bindata(sc,tdata,'OutputType','BinNumber'); disp(bdata)
CustID CustAge TmAtAddress ResStatus EmpStatus CustIncome TmWBank OtherCC AMBalance UtilRate status ______ _______ ___________ _________ _________ __________ _______ _______ _________ ________ ______ 11 NaN 2 1 1 3 3 2 1 2 1 12 6 NaN 3 1 6 2 2 1 1 0 13 8 2 NaN 1 7 1 1 1 1 0 14 4 2 3 NaN 5 3 1 1 2 0
And when 'OutputType'
is set to 'Categorical'
, missing values are set to '<undefined>'
:
bdata = bindata(sc,tdata,'OutputType','Categorical'); disp(bdata)
CustID CustAge TmAtAddress ResStatus EmpStatus CustIncome TmWBank OtherCC AMBalance UtilRate status ______ ___________ ___________ ___________ ___________ _____________ _________ _______ _____________ ___________ ______ 11 <undefined> [23,83) Tenant Unknown [33000,35000) [23,45) Yes [-Inf,558.88) [0.04,0.36) 1 12 [48,51) <undefined> Other Unknown [42000,47000) [12,23) Yes [-Inf,558.88) [-Inf,0.04) 0 13 [58,Inf] [23,83) <undefined> Unknown [47000,Inf] [-Inf,12) No [-Inf,558.88) [-Inf,0.04) 0 14 [40,46) [23,83) Other <undefined> [40000,42000) [23,45) No [-Inf,558.88) [0.04,0.36) 0
Apply a Weight of Evidence (WOE) Transformation to Data
bindata
supports the following types of WOE transformation:
When the
'OutputType'
name-value argument is set to'WOE'
,bindata
simply applies the WOE transformation to all predictors and keeps the rest of the variables in the original data in place and unchanged.When the
'OutputType'
name-value pair argument is set to'WOEModelInput'
,bindata
returns a table that can be used directly as an input for fitting a logistic regression model for the scorecard. In this case,bindata
:Applies WOE transformation to all predictors.
Returns predictor variables, but no
IDVar
or unused variables are included in the output.Includes the mapped response variable as the last column.
The
fitmodel
function callsbindata
internally using the'WOEModelInput'
option to fit the logistic regression model for thecreditscorecard
model.
Create a creditscorecard
object using the CreditCardData.mat
file to load the data
(using a dataset from Refaat 2011). Use the 'IDVar'
argument to indicate that 'CustID'
contains ID information and should not be included as a predictor variable.
load CreditCardData 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' '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]
Perform automatic binning.
sc = autobinning(sc);
Show the bin information for 'CustAge'
.
bininfo(sc,'CustAge')
ans=8×6 table
Bin Good Bad Odds WOE InfoValue
_____________ ____ ___ ______ _________ _________
{'[-Inf,33)'} 70 53 1.3208 -0.42622 0.019746
{'[33,37)' } 64 47 1.3617 -0.39568 0.015308
{'[37,40)' } 73 47 1.5532 -0.26411 0.0072573
{'[40,46)' } 174 94 1.8511 -0.088658 0.001781
{'[46,48)' } 61 25 2.44 0.18758 0.0024372
{'[48,58)' } 263 105 2.5048 0.21378 0.013476
{'[58,Inf]' } 98 26 3.7692 0.62245 0.0352
{'Totals' } 803 397 2.0227 NaN 0.095205
These are the first 10 age values in the original data, used to create the creditscorecard
object.
data(1:10,'CustAge')
ans=10×1 table
CustAge
_______
53
61
47
50
68
65
34
50
50
49
Convert the test data to WOE values. To do this, set the bindata
name-value pair argument for 'OutputType'
to 'WOE'
.
bdata = bindata(sc,'OutputType','WOE');
These are the first 10 binned ages, in WOE format. The ages are mapped to the WOE values displayed internally by bininfo
.
bdata(1:10,'CustAge')
ans=10×1 table
CustAge
________
0.21378
0.62245
0.18758
0.21378
0.62245
0.62245
-0.39568
0.21378
0.21378
0.21378
These are the first 10 binned ages, in WOE format. The ages are mapped to the WOE values displayed internally by bininfo
.
bdata(1:10,'CustAge')
ans=10×1 table
CustAge
________
0.21378
0.62245
0.18758
0.21378
0.62245
0.62245
-0.39568
0.21378
0.21378
0.21378
The size of the original data and the size of bdata
output are the same because bindata
leaves unused variables (such as 'IDVar'
) unchanged and in place.
whos data bdata
Name Size Bytes Class Attributes bdata 1200x11 109307 table data 1200x11 85035 table
The response values are the same in the original data and in the binned data because, by default, bindata
does not modify response values.
disp([data.status(1:10) bdata.status(1:10)])
0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 1
When fitting a logistic regression model with WOE data, set the 'OutputType'
name-value pair argument to 'WOEModelInput'
.
bdata = bindata(sc,'OutputType','WOEModelInput');
The binned predictor data is the same as when the 'OutputType'
name-value pair argument is set to 'WOE'
.
bdata(1:10,'CustAge')
ans=10×1 table
CustAge
________
0.21378
0.62245
0.18758
0.21378
0.62245
0.62245
-0.39568
0.21378
0.21378
0.21378
However, the size of the original data and the size of bdata
output are different. This is because bindata
removes unused variables (such as 'IDVar'
).
whos data bdata
Name Size Bytes Class Attributes bdata 1200x10 99455 table data 1200x11 85035 table
The response values are also modified in this case and are mapped so that "Good" is 1
and "Bad" is 0
.
disp([data.status(1:10) bdata.status(1:10)])
0 1 0 1 0 1 0 1 0 1 0 1 1 0 0 1 1 0 1 0
Input Arguments
sc
— Credit scorecard model
creditscorecard
object
Credit scorecard model, specified as a
creditscorecard
object. Use creditscorecard
to create
a creditscorecard
object.
data
— Data to bin given the rules set in creditscorecard
object
table
Data to bin given the rules set in the
creditscorecard
object, specified using a table.
By default, data
is set to the
creditscorecard
object's raw data.
Before creating a creditscorecard
object, perform a
data preparation task to have an appropriately structured
data
as input to a
creditscorecard
object.
Data Types: table
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: bdata =
bindata(sc,'OutputType','WOE','ResponseFormat','Mapped')
OutputType
— Output format
'BinNumber'
(default) | character vector with values 'BinNumber'
,
'Categorical'
,
'WOE'
Output format, specified as the comma-separated pair consisting of
'OutputType'
and a character vector with the
following values:
BinNumber
— Returns the bin numbers corresponding to each observation.Categorical
— Returns the bin label corresponding to each observation.WOE
— Returns the Weight of Evidence (WOE) corresponding to each observation.WOEModelInput
— Use this option when fitting a model. This option:Returns the Weight of Evidence (WOE) corresponding to each observation.
Returns predictor variables, but no
IDVar
or unused variables are included in the output.Discards any predictors whose bins have
Inf
orNaN
WOE values.Includes the mapped response variable as the last column.
Note
When the
bindata
name-value pair argument'OutputType'
is set to'WOEModelInput'
, thebdata
output only contains the columns corresponding to predictors whose bins do not haveInf
orNaN
Weight of Evidence (WOE) values, andbdata
includes the mapped response as the last column.Missing data (if any) are included in the
bdata
output as missing data as well, and do not influence the rules to discard predictors when'OutputType'
is set to'WOEModelInput'
.
Data Types: char
ResponseFormat
— Response values format
'RawData'
(default) | character vector with values 'RawData'
,
'Mapped'
Response values format, specified as the comma-separated pair
consisting of 'ResponseFormat'
and a character vector
with the following values:
RawData
— The response variable is copied unchanged into thebdata
output.Mapped
— The response values are modified (if necessary) so that "Good" is mapped to1
, and "Bad" is mapped to0
.
Data Types: char
Output Arguments
bdata
— Binned predictor variables
table
Binned predictor variables, returned as a table. This is a table of
the same size (see exception in the following Note) as the data input,
but only the predictors specified in the
creditscorecard
object's
PredictorVars
property are binned and the
remaining ones are unchanged.
Note
When the bindata
name-value pair argument
'OutputType'
is set to
'WOEModelInput'
, the
bdata
output only contains the columns
corresponding to predictors whose bins do not have
Inf
or NaN
Weight of
Evidence (WOE) values, and bdata
includes the
mapped response as the last column.
Missing data (if any) are included in the
bdata
output as missing data as well, and
do not influence the rules to discard predictors when
'OutputType'
is set to
'WOEModelInput'
.
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.
Version History
Introduced in R2014b
See Also
creditscorecard
| autobinning
| bininfo
| predictorinfo
| modifypredictor
| plotbins
| modifybins
| fitmodel
| displaypoints
| formatpoints
| score
| setmodel
| probdefault
| validatemodel
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