autobinning
Perform automatic binning of given predictors
Description
sc = autobinning(sc)
Automatic binning finds binning maps or rules to bin numeric data and to group
categories of categorical data. The binning rules are stored in the
creditscorecard object. To apply the binning rules to the
creditscorecard object data, or to a new dataset, use
bindata.
sc = autobinning(sc,PredictorNames)PredictorNames.
Automatic binning finds binning maps or rules to bin numeric data and to group
categories of categorical data. The binning rules are stored in the
creditscorecard object. To apply the binning rules to the
creditscorecard object data, or to a new dataset, use
bindata.
sc = autobinning(___,Name,Value)PredictorNames using optional name-value pair
arguments. See the name-value argument Algorithm for a
description of the supported binning algorithms.
Automatic binning finds binning maps or rules to bin numeric data and to group
categories of categorical data. The binning rules are stored in the
creditscorecard object. To apply the binning rules to the
creditscorecard object data, or to a new dataset, use
bindata.
Examples
Perform Automatic Binning Using the Defaults
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');
Perform automatic binning using the default options. By default, autobinning bins all predictors and uses the Monotone algorithm.
sc = autobinning(sc);
Use bininfo to display the binned data for the predictor CustAge.
bi = bininfo(sc, 'CustAge')bi=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
Use plotbins to display the histogram and WOE curve for the predictor CustAge.
plotbins(sc,'CustAge')
Perform Automatic Binning with a Named Predictor Using the Defaults
Create a creditscorecard object using the CreditCardData.mat file to load the data (using a dataset from Refaat 2011).
load CreditCardData
sc = creditscorecard(data);Perform automatic binning for the predictor CustIncome using the default options. By default, autobinning uses the Monotone algorithm.
sc = autobinning(sc,'CustIncome');Use bininfo to display the binned data.
bi = bininfo(sc, 'CustIncome')bi=8×6 table
Bin Good Bad Odds WOE InfoValue
_________________ ____ ___ _______ _________ __________
{'[-Inf,29000)' } 53 58 0.91379 -0.79457 0.06364
{'[29000,33000)'} 74 49 1.5102 -0.29217 0.0091366
{'[33000,35000)'} 68 36 1.8889 -0.06843 0.00041042
{'[35000,40000)'} 193 98 1.9694 -0.026696 0.00017359
{'[40000,42000)'} 68 34 2 -0.011271 1.0819e-05
{'[42000,47000)'} 164 66 2.4848 0.20579 0.0078175
{'[47000,Inf]' } 183 56 3.2679 0.47972 0.041657
{'Totals' } 803 397 2.0227 NaN 0.12285
Perform Automatic Binning Using Two Name-Value Pair Arguments
Create a creditscorecard object using the CreditCardData.mat file to load the data (using a dataset from Refaat 2011).
load CreditCardData
sc = creditscorecard(data);Perform automatic binning for the predictor CustIncome using the Monotone algorithm with the initial number of bins set to 20. This example explicitly sets both the Algorithm and the AlgorithmOptions name-value arguments.
AlgoOptions = {'InitialNumBins',20};
sc = autobinning(sc,'CustIncome','Algorithm','Monotone','AlgorithmOptions',...
AlgoOptions);Use bininfo to display the binned data. Here, the cut points, which delimit the bins, are also displayed.
[bi,cp] = bininfo(sc,'CustIncome')bi=11×6 table
Bin Good Bad Odds WOE InfoValue
_________________ ____ ___ _______ _________ __________
{'[-Inf,19000)' } 2 3 0.66667 -1.1099 0.0056227
{'[19000,29000)'} 51 55 0.92727 -0.77993 0.058516
{'[29000,31000)'} 29 26 1.1154 -0.59522 0.017486
{'[31000,34000)'} 80 42 1.9048 -0.060061 0.0003704
{'[34000,35000)'} 33 17 1.9412 -0.041124 7.095e-05
{'[35000,40000)'} 193 98 1.9694 -0.026696 0.00017359
{'[40000,42000)'} 68 34 2 -0.011271 1.0819e-05
{'[42000,43000)'} 39 16 2.4375 0.18655 0.001542
{'[43000,47000)'} 125 50 2.5 0.21187 0.0062972
{'[47000,Inf]' } 183 56 3.2679 0.47972 0.041657
{'Totals' } 803 397 2.0227 NaN 0.13175
cp = 9×1
19000
29000
31000
34000
35000
40000
42000
43000
47000
Perform Automatic Binning Using Multiple Name-Value Pair Arguments
This example shows how to use the autobinning default Monotone algorithm and the AlgorithmOptions name-value pair arguments associated with the Monotone algorithm. The AlgorithmOptions for the Monotone algorithm are three name-value pair parameters: ‘InitialNumBins', 'Trend', and 'SortCategories'. 'InitialNumBins' and 'Trend' are applicable for numeric predictors and 'Trend' and 'SortCategories' are applicable for categorical predictors.
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');
Perform automatic binning for the numeric predictor CustIncome using the Monotone algorithm with 20 bins. This example explicitly sets both the Algorithm argument and the AlgorithmOptions name-value arguments for 'InitialNumBins' and 'Trend'.
AlgoOptions = {'InitialNumBins',20,'Trend','Increasing'};
sc = autobinning(sc,'CustIncome','Algorithm','Monotone',...
'AlgorithmOptions',AlgoOptions);Use bininfo to display the binned data.
bi = bininfo(sc,'CustIncome')bi=11×6 table
Bin Good Bad Odds WOE InfoValue
_________________ ____ ___ _______ _________ __________
{'[-Inf,19000)' } 2 3 0.66667 -1.1099 0.0056227
{'[19000,29000)'} 51 55 0.92727 -0.77993 0.058516
{'[29000,31000)'} 29 26 1.1154 -0.59522 0.017486
{'[31000,34000)'} 80 42 1.9048 -0.060061 0.0003704
{'[34000,35000)'} 33 17 1.9412 -0.041124 7.095e-05
{'[35000,40000)'} 193 98 1.9694 -0.026696 0.00017359
{'[40000,42000)'} 68 34 2 -0.011271 1.0819e-05
{'[42000,43000)'} 39 16 2.4375 0.18655 0.001542
{'[43000,47000)'} 125 50 2.5 0.21187 0.0062972
{'[47000,Inf]' } 183 56 3.2679 0.47972 0.041657
{'Totals' } 803 397 2.0227 NaN 0.13175
Perform Automatic Binning for Multiple Predictors
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');
Perform automatic binning for the predictor CustIncome and CustAge using the default Monotone algorithm with AlgorithmOptions for InitialNumBins and Trend.
AlgoOptions = {'InitialNumBins',20,'Trend','Increasing'};
sc = autobinning(sc,{'CustAge','CustIncome'},'Algorithm','Monotone',...
'AlgorithmOptions',AlgoOptions);Use bininfo to display the binned data.
bi1 = bininfo(sc, 'CustIncome')bi1=11×6 table
Bin Good Bad Odds WOE InfoValue
_________________ ____ ___ _______ _________ __________
{'[-Inf,19000)' } 2 3 0.66667 -1.1099 0.0056227
{'[19000,29000)'} 51 55 0.92727 -0.77993 0.058516
{'[29000,31000)'} 29 26 1.1154 -0.59522 0.017486
{'[31000,34000)'} 80 42 1.9048 -0.060061 0.0003704
{'[34000,35000)'} 33 17 1.9412 -0.041124 7.095e-05
{'[35000,40000)'} 193 98 1.9694 -0.026696 0.00017359
{'[40000,42000)'} 68 34 2 -0.011271 1.0819e-05
{'[42000,43000)'} 39 16 2.4375 0.18655 0.001542
{'[43000,47000)'} 125 50 2.5 0.21187 0.0062972
{'[47000,Inf]' } 183 56 3.2679 0.47972 0.041657
{'Totals' } 803 397 2.0227 NaN 0.13175
bi2 = bininfo(sc, 'CustAge')bi2=8×6 table
Bin Good Bad Odds WOE InfoValue
_____________ ____ ___ ______ _________ __________
{'[-Inf,35)'} 93 76 1.2237 -0.50255 0.038003
{'[35,40)' } 114 71 1.6056 -0.2309 0.0085141
{'[40,42)' } 52 30 1.7333 -0.15437 0.0016687
{'[42,44)' } 58 32 1.8125 -0.10971 0.00091888
{'[44,47)' } 97 51 1.902 -0.061533 0.00047174
{'[47,62)' } 333 130 2.5615 0.23619 0.020605
{'[62,Inf]' } 56 7 8 1.375 0.071647
{'Totals' } 803 397 2.0227 NaN 0.14183
Perform Automatic Binning for a Categorical Predictor Using the Defaults
Create a creditscorecard object using the CreditCardData.mat file to load the data (using a dataset from Refaat 2011).
load CreditCardData
sc = creditscorecard(data);Perform automatic binning for the predictor that is a categorical predictor called ResStatus using the default options. By default, autobinning uses the Monotone algorithm.
sc = autobinning(sc,'ResStatus');Use bininfo to display the binned data.
bi = bininfo(sc, 'ResStatus')bi=4×6 table
Bin Good Bad Odds WOE InfoValue
______________ ____ ___ ______ _________ _________
{'Tenant' } 307 167 1.8383 -0.095564 0.0036638
{'Home Owner'} 365 177 2.0621 0.019329 0.0001682
{'Other' } 131 53 2.4717 0.20049 0.0059418
{'Totals' } 803 397 2.0227 NaN 0.0097738
Perform Automatic Binning for a Categorical Predictor Using Name-Value Pair Arguments
This example shows how to modify the data (for this example only) to illustrate binning categorical predictors using the Monotone algorithm.
Create a creditscorecard object using the CreditCardData.mat file to load the data (using a dataset from Refaat 2011).
load CreditCardDataAdd two new categories and updating the response variable.
newdata = data; rng('default'); %for reproducibility Predictor = 'ResStatus'; Status = newdata.status; NumObs = length(newdata.(Predictor)); Ind1 = randi(NumObs,100,1); Ind2 = randi(NumObs,100,1); newdata.(Predictor)(Ind1) = 'Subtenant'; newdata.(Predictor)(Ind2) = 'CoOwner'; Status(Ind1) = randi(2,100,1)-1; Status(Ind2) = randi(2,100,1)-1; newdata.status = Status;
Update the creditscorecard object using the newdata and plot the bins for a later comparison.
scnew = creditscorecard(newdata,'IDVar','CustID'); [bi,cg] = bininfo(scnew,Predictor)
bi=6×6 table
Bin Good Bad Odds WOE InfoValue
______________ ____ ___ ______ ________ _________
{'Home Owner'} 308 154 2 0.092373 0.0032392
{'Tenant' } 264 136 1.9412 0.06252 0.0012907
{'Other' } 109 49 2.2245 0.19875 0.0050386
{'Subtenant' } 42 42 1 -0.60077 0.026813
{'CoOwner' } 52 44 1.1818 -0.43372 0.015802
{'Totals' } 775 425 1.8235 NaN 0.052183
cg=5×2 table
Category BinNumber
______________ _________
{'Home Owner'} 1
{'Tenant' } 2
{'Other' } 3
{'Subtenant' } 4
{'CoOwner' } 5
plotbins(scnew,Predictor)
Perform automatic binning for the categorical Predictor using the default Monotone algorithm with the AlgorithmOptions name-value pair arguments for 'SortCategories' and 'Trend'.
AlgoOptions = {'SortCategories','Goods','Trend','Increasing'};
scnew = autobinning(scnew,Predictor,'Algorithm','Monotone',...
'AlgorithmOptions',AlgoOptions);Use bininfo to display the bin information. The second output parameter 'cg' captures the bin membership, which is the bin number that each group belongs to.
[bi,cg] = bininfo(scnew,Predictor)
bi=4×6 table
Bin Good Bad Odds WOE InfoValue
__________ ____ ___ ______ ________ _________
{'Group1'} 42 42 1 -0.60077 0.026813
{'Group2'} 52 44 1.1818 -0.43372 0.015802
{'Group3'} 681 339 2.0088 0.096788 0.0078459
{'Totals'} 775 425 1.8235 NaN 0.05046
cg=5×2 table
Category BinNumber
______________ _________
{'Subtenant' } 1
{'CoOwner' } 2
{'Other' } 3
{'Tenant' } 3
{'Home Owner'} 3
Plot bins and compare with the histogram plotted pre-binning.
plotbins(scnew,Predictor)
Perform Automatic Binning When Using Missing Data
Create a creditscorecard object using the CreditCardData.mat file to load the dataMissing with 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
fprintf('Number of rows: %d\n',height(dataMissing))Number of rows: 1200
fprintf('Number of missing values CustAge: %d\n',sum(ismissing(dataMissing.CustAge)))Number of missing values CustAge: 30
fprintf('Number of missing values ResStatus: %d\n',sum(ismissing(dataMissing.ResStatus)))Number of missing values ResStatus: 40
Use creditscorecard with the name-value argument 'BinMissingData' set to true to bin the missing numeric and categorical data in a separate bin.
sc = creditscorecard(dataMissing,'BinMissingData',true);
disp(sc) creditscorecard with properties:
GoodLabel: 0
ResponseVar: 'status'
WeightsVar: ''
VarNames: {'CustID' 'CustAge' 'TmAtAddress' 'ResStatus' 'EmpStatus' 'CustIncome' 'TmWBank' 'OtherCC' 'AMBalance' 'UtilRate' 'status'}
NumericPredictors: {'CustID' 'CustAge' 'TmAtAddress' 'CustIncome' 'TmWBank' 'AMBalance' 'UtilRate'}
CategoricalPredictors: {'ResStatus' 'EmpStatus' 'OtherCC'}
BinMissingData: 1
IDVar: ''
PredictorVars: {'CustID' 'CustAge' 'TmAtAddress' 'ResStatus' 'EmpStatus' 'CustIncome' 'TmWBank' 'OtherCC' 'AMBalance' 'UtilRate'}
Data: [1200x11 table]
Perform automatic binning using the Merge algorithm.
sc = autobinning(sc,'Algorithm','Merge');
Display bin information for numeric data for 'CustAge' that includes missing data in a separate bin labelled <missing> and this is the last bin. No matter what binning algorithm is used in autobinning, the algorithm operates on the non-missing data and the bin for the <missing> numeric values for a predictor is always the last bin.
[bi,cp] = bininfo(sc,'CustAge');
disp(bi) Bin Good Bad Odds WOE InfoValue
_____________ ____ ___ _______ ________ __________
{'[-Inf,32)'} 56 39 1.4359 -0.34263 0.0097643
{'[32,33)' } 13 13 1 -0.70442 0.011663
{'[33,34)' } 9 11 0.81818 -0.90509 0.014934
{'[34,65)' } 677 317 2.1356 0.054351 0.002424
{'[65,Inf]' } 29 6 4.8333 0.87112 0.018295
{'<missing>'} 19 11 1.7273 -0.15787 0.00063885
{'Totals' } 803 397 2.0227 NaN 0.057718
plotbins(sc,'CustAge')
Display bin information for categorical data for 'ResStatus' that includes missing data in a separate bin labelled <missing> and this is the last bin. No matter what binning algorithm is used in autobinning, the algorithm operates on the non-missing data and the bin for the <missing> categorical values for a predictor is always the last bin.
[bi,cg] = bininfo(sc,'ResStatus');
disp(bi) Bin Good Bad Odds WOE InfoValue
_____________ ____ ___ ______ _________ __________
{'Group1' } 648 332 1.9518 -0.035663 0.0010449
{'Group2' } 128 52 2.4615 0.19637 0.0055808
{'<missing>'} 27 13 2.0769 0.026469 2.3248e-05
{'Totals' } 803 397 2.0227 NaN 0.0066489
plotbins(sc,'ResStatus')
Perform Automatic Binning Using the Split Algorithm
This example demonstrates using the 'Split' algorithm with categorical and numeric predictors. Load the CreditCardData.mat dataset and modify so that it contains four categories for the predictor 'ResStatus' to demonstrate how the split algorithm works.
load CreditCardData.mat x = data.ResStatus; Ind = find(x == 'Tenant'); Nx = length(Ind); x(Ind(1:floor(Nx/3))) = 'Subletter'; data.ResStatus = x;
Create a creditscorecard and use bininfo to display the 'Statistics'.
sc = creditscorecard(data,'IDVar','CustID'); [bi1,cg1] = bininfo(sc,'ResStatus','Statistics',{'Odds','WOE','InfoValue'}); disp(bi1)
Bin Good Bad Odds WOE InfoValue
______________ ____ ___ ______ _________ __________
{'Home Owner'} 365 177 2.0621 0.019329 0.0001682
{'Tenant' } 204 112 1.8214 -0.1048 0.0029415
{'Other' } 131 53 2.4717 0.20049 0.0059418
{'Subletter' } 103 55 1.8727 -0.077023 0.00079103
{'Totals' } 803 397 2.0227 NaN 0.0098426
disp(cg1)
Category BinNumber
______________ _________
{'Home Owner'} 1
{'Tenant' } 2
{'Other' } 3
{'Subletter' } 4
Using the Split Algorithm with a Categorical Predictor
Apply presorting to the 'ResStatus' category using the default sorting by 'Odds' and specify the 'Split' algorithm.
sc = autobinning(sc,'ResStatus', 'Algorithm', 'split','AlgorithmOptions',... {'Measure','gini','SortCategories','odds','Tolerance',1e4}); [bi2,cg2] = bininfo(sc,'ResStatus','Statistics',{'Odds','WOE','InfoValue'}); disp(bi2)
Bin Good Bad Odds WOE InfoValue
__________ ____ ___ ______ ___ _________
{'Group1'} 803 397 2.0227 0 0
{'Totals'} 803 397 2.0227 NaN 0
disp(cg2)
Category BinNumber
______________ _________
{'Tenant' } 1
{'Subletter' } 1
{'Home Owner'} 1
{'Other' } 1
Using the Split Algorithm with a Numeric Predictor
To demonstrate a split for the numeric predictor, 'TmAtAddress', first use autobinning with the default 'Monotone' algorithm.
sc = autobinning(sc,'TmAtAddress'); bi3 = bininfo(sc,'TmAtAddress','Statistics',{'Odds','WOE','InfoValue'}); disp(bi3)
Bin Good Bad Odds WOE InfoValue
_____________ ____ ___ ______ _________ __________
{'[-Inf,23)'} 239 129 1.8527 -0.087767 0.0023963
{'[23,83)' } 480 232 2.069 0.02263 0.00030269
{'[83,Inf]' } 84 36 2.3333 0.14288 0.00199
{'Totals' } 803 397 2.0227 NaN 0.004689
Then use autobinning with the 'Split' algorithm.
sc = autobinning(sc,'TmAtAddress','Algorithm', 'Split'); bi4 = bininfo(sc,'TmAtAddress','Statistics',{'Odds','WOE','InfoValue'}); disp(bi4)
Bin Good Bad Odds WOE InfoValue
____________ ____ ___ _______ _________ __________
{'[-Inf,4)'} 20 12 1.6667 -0.19359 0.0010299
{'[4,5)' } 4 7 0.57143 -1.264 0.015991
{'[5,23)' } 215 110 1.9545 -0.034261 0.00031973
{'[23,33)' } 130 39 3.3333 0.49955 0.0318
{'[33,Inf]'} 434 229 1.8952 -0.065096 0.0023664
{'Totals' } 803 397 2.0227 NaN 0.051507
Perform Automatic Binning Using the Merge Algorithm
Load the CreditCardData.mat dataset. This example demonstrates using the 'Merge' algorithm with categorical and numeric predictors.
load CreditCardData.matUsing the Merge Algorithm with a Categorical Predictor
To merge a categorical predictor, create a creditscorecard using default sorting by 'Odds' and then use bininfo on the categorical predictor 'ResStatus'.
sc = creditscorecard(data,'IDVar','CustID'); [bi1,cg1] = bininfo(sc,'ResStatus','Statistics',{'Odds','WOE','InfoValue'}); disp(bi1);
Bin Good Bad Odds WOE InfoValue
______________ ____ ___ ______ _________ _________
{'Home Owner'} 365 177 2.0621 0.019329 0.0001682
{'Tenant' } 307 167 1.8383 -0.095564 0.0036638
{'Other' } 131 53 2.4717 0.20049 0.0059418
{'Totals' } 803 397 2.0227 NaN 0.0097738
disp(cg1);
Category BinNumber
______________ _________
{'Home Owner'} 1
{'Tenant' } 2
{'Other' } 3
Use autobinning and specify the 'Merge' algorithm.
sc = autobinning(sc,'ResStatus','Algorithm', 'Merge'); [bi2,cg2] = bininfo(sc,'ResStatus','Statistics',{'Odds','WOE','InfoValue'}); disp(bi2)
Bin Good Bad Odds WOE InfoValue
__________ ____ ___ ______ _________ _________
{'Group1'} 672 344 1.9535 -0.034802 0.0010314
{'Group2'} 131 53 2.4717 0.20049 0.0059418
{'Totals'} 803 397 2.0227 NaN 0.0069732
disp(cg2)
Category BinNumber
______________ _________
{'Tenant' } 1
{'Home Owner'} 1
{'Other' } 2
Using the Merge Algorithm with a Numeric Predictor
To demonstrate a merge for the numeric predictor, 'TmAtAddress', first use autobinning with the default 'Monotone' algorithm.
sc = autobinning(sc,'TmAtAddress'); bi3 = bininfo(sc,'TmAtAddress','Statistics',{'Odds','WOE','InfoValue'}); disp(bi3)
Bin Good Bad Odds WOE InfoValue
_____________ ____ ___ ______ _________ __________
{'[-Inf,23)'} 239 129 1.8527 -0.087767 0.0023963
{'[23,83)' } 480 232 2.069 0.02263 0.00030269
{'[83,Inf]' } 84 36 2.3333 0.14288 0.00199
{'Totals' } 803 397 2.0227 NaN 0.004689
Then use autobinning with the 'Merge' algorithm.
sc = autobinning(sc,'TmAtAddress','Algorithm', 'Merge'); bi4 = bininfo(sc,'TmAtAddress','Statistics',{'Odds','WOE','InfoValue'}); disp(bi4)
Bin Good Bad Odds WOE InfoValue
_____________ ____ ___ _______ _________ __________
{'[-Inf,28)'} 303 152 1.9934 -0.014566 8.0646e-05
{'[28,30)' } 27 2 13.5 1.8983 0.054264
{'[30,98)' } 428 216 1.9815 -0.020574 0.00022794
{'[98,106)' } 11 13 0.84615 -0.87147 0.016599
{'[106,Inf]'} 34 14 2.4286 0.18288 0.0012942
{'Totals' } 803 397 2.0227 NaN 0.072466
Input Arguments
Output Arguments
More About
References
[1] Anderson, R. The Credit Scoring Toolkit. Oxford University Press, 2007.
[2] Kerber, R. "ChiMerge: Discretization of Numeric Attributes." AAAI-92 Proceedings. 1992.
[3] Liu, H., et. al. Data Mining, Knowledge, and Discovery. Vol 6. Issue 4. October 2002, pp. 393-423.
[4] Refaat, M. Data Preparation for Data Mining Using SAS. Morgan Kaufmann, 2006.
[5] Refaat, M. Credit Risk Scorecards: Development and Implementation Using SAS. lulu.com, 2011.
[6] Thomas, L., et al. Credit Scoring and Its Applications. Society for Industrial and Applied Mathematics, 2002.
Version History
Introduced in R2014b
See Also
creditscorecard | bininfo | predictorinfo | modifypredictor | modifybins | bindata | plotbins | fitmodel | displaypoints | formatpoints | score | setmodel | probdefault | validatemodel
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