autobinning

Perform automatic binning of given predictors

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Syntax

sc = autobinning(sc)
sc = autobinning(sc,PredictorNames)
sc = autobinning(___,Name,Value)

Description

sc = autobinning(sc) performs automatic binning of all predictors.

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.

example

sc = autobinning(sc,PredictorNames) performs automatic binning of the predictors given in 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.

example

sc = autobinning(___,Name,Value) performs automatic binning of the predictors given in 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.

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).

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')

Figure contains an axes object. The axes object with title CustAge, ylabel WOE contains 3 objects of type bar, line. These objects represent Good, Bad.

Open Live Script

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
Open Live Script

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
Open Live Script

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
Open Live Script

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
Open Live Script

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
Open Live Script

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 CreditCardData

Add 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)

Figure contains an axes object. The axes object with title ResStatus, ylabel WOE contains 3 objects of type bar, line. These objects represent Good, Bad.

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)

Figure contains an axes object. The axes object with title ResStatus, ylabel WOE contains 3 objects of type bar, line. These objects represent Good, Bad.

Open Live Script

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: [1200×11 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')

Figure contains an axes object. The axes object with title CustAge, ylabel WOE contains 3 objects of type bar, line. These objects represent Good, Bad.

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')

Figure contains an axes object. The axes object with title ResStatus, ylabel WOE contains 3 objects of type bar, line. These objects represent Good, Bad.

Open Live Script

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
Open Live Script

Load the CreditCardData.mat dataset. This example demonstrates using the 'Merge' algorithm with categorical and numeric predictors.

load CreditCardData.mat

Using 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

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Credit scorecard model, specified as a creditscorecard object. Use creditscorecard to create a creditscorecard object.

Predictor or predictors names to automatically bin, specified as a character vector or a cell array of character vectors containing the name of the predictor or predictors. PredictorNames are case-sensitive and when no PredictorNames are defined, all predictors in the PredictorVars property of the creditscorecard object are binned.

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 = autobinning(sc,'Algorithm','EqualFrequency')

Algorithm selection, specified as the comma-separated pair consisting of 'Algorithm' and a character vector indicating which algorithm to use. The same algorithm is used for all predictors in PredictorNames. Possible values are:

  • 'Monotone' — (default) Monotone Adjacent Pooling Algorithm (MAPA), also known as Maximum Likelihood Monotone Coarse Classifier (MLMCC). Supervised optimal binning algorithm that aims to find bins with a monotone Weight-Of-Evidence (WOE) trend. This algorithm assumes that only neighboring attributes can be grouped. Thus, for categorical predictors, categories are sorted before applying the algorithm (see 'SortCategories' option for AlgorithmOptions). For more information, see Monotone.

  • 'Split' — Supervised binning algorithm, where a measure is used to split the data into bins. The measures supported by 'Split' are gini, chi2, infovalue, and entropy. The resulting split must be such that the gain in the information function is maximized. For more information on these measures, see AlgorithmOptions and Split.

  • 'Merge' — Supervised automatic binning algorithm, where a measure is used to merge bins into buckets. The measures supported by 'Merge' are chi2, gini, infovalue, and entropy. The resulting merging must be such that any pair of adjacent bins is statistically different from each other, according to the chosen measure. For more information on these measures, see AlgorithmOptions and Merge.

  • 'EqualFrequency' — Unsupervised algorithm that divides the data into a predetermined number of bins that contain approximately the same number of observations. This algorithm is also known as “equal height” or “equal depth.” For categorical predictors, categories are sorted before applying the algorithm (see 'SortCategories' option for AlgorithmOptions). For more information, see Equal Frequency.

  • 'EqualWidth' — Unsupervised algorithm that divides the range of values in the domain of the predictor variable into a predetermined number of bins of “equal width.” For numeric data, the width is measured as the distance between bin edges. For categorical data, width is measured as the number of categories within a bin. For categorical predictors, categories are sorted before applying the algorithm (see 'SortCategories' option for AlgorithmOptions). For more information, see Equal Width.

Data Types: char

Algorithm options for the selected Algorithm, specified as the comma-separated pair consisting of 'AlgorithmOptions' and a cell array. Possible values are:

  • For Monotone algorithm:

    • {'InitialNumBins',n} — Initial number (n) of bins (default is 10). 'InitialNumBins' must be an integer > 2. Used for numeric predictors only.

    • {'Trend','TrendOption'} — Determines whether the Weight-Of-Evidence (WOE) monotonic trend is expected to be increasing or decreasing. The values for 'TrendOption' are:

      • 'Auto' — (Default) Automatically determines if the WOE trend is increasing or decreasing.

      • 'Increasing' — Look for an increasing WOE trend.

      • 'Decreasing' — Look for a decreasing WOE trend.

      The value of the optional input parameter 'Trend' does not necessarily reflect that of the resulting WOE curve. The parameter 'Trend' tells the algorithm to “look for” an increasing or decreasing trend, but the outcome may not show the desired trend. For example, the algorithm cannot find a decreasing trend when the data actually has an increasing WOE trend. For more information on the 'Trend' option, see Monotone.

    • {'SortCategories','SortOption'} — Used for categorical predictors only. Used to determine how the predictor categories are sorted as a preprocessing step before applying the algorithm. The values of 'SortOption' are:

      • 'Odds' — (default) The categories are sorted by order of increasing values of odds, defined as the ratio of “Good” to “Bad” observations, for the given category.

      • 'Goods' — The categories are sorted by order of increasing values of “Good.”

      • 'Bads' — The categories are sorted by order of increasing values of “Bad.”

      • 'Totals' — The categories are sorted by order of increasing values of total number of observations (“Good” plus “Bad”).

      • 'None' — No sorting is applied. The existing order of the categories is unchanged before applying the algorithm. (The existing order of the categories can be seen in the category grouping optional output from bininfo.)

      For more information, see Sort Categories

  • For Split algorithm:

    • {'InitialNumBins',n} — Specifies an integer that determines the number (n >0) of bins that the predictor is initially binned into before splitting. Valid for numeric predictors only. Default is 50.

    • {'Measure',MeasureName} — Specifies the measure where 'MeasureName' is one of the following:'Gini' (default), 'Chi2', 'InfoValue', or 'Entropy'.

    • {'MinBad',n} — Specifies the minimum number n (n>=0) of Bads per bin. The default value is 1, to avoid pure bins.

    • {'MaxBad',n} — Specifies the maximum number n (n>=0) of Bads per bin. The default value is Inf.

    • {'MinGood',n} — Specifies the minimum number n (n>=0) of Goods per bin. The default value is 1, to avoid pure bins.

    • {'MaxGood',n} — Specifies the maximum number n (n>=0) of Goods per bin. The default value is Inf.

    • {'MinCount',n} — Specifies the minimum number n (n>=0) of observations per bin. The default value is 1, to avoid empty bins.

    • {'MaxCount',n} — Specifies the maximum number n (n>=0) of observations per bin. The default value is Inf.

    • {'MaxNumBins',n} — Specifies the maximum number n (n>=2) of bins resulting from the splitting. The default value is 5.

    • {'Tolerance',Tol} — Specifies the minimum gain (>0) in the information function, during the iteration scheme, to select the cut-point that maximizes the gain. The default is 1e4.

    • {'Significance',n} — Significance level threshold for the chi-square statistic, above which splitting happens. Values are in the interval [0,1]. Default is 0.9 (90% significance level).

    • {'SortCategories','SortOption'} — Used for categorical predictors only. Used to determine how the predictor categories are sorted as a preprocessing step before applying the algorithm. The values of 'SortOption' are:

      • 'Goods' — The categories are sorted by order of increasing values of “Good.”

      • 'Bads' — The categories are sorted by order of increasing values of “Bad.”

      • 'Odds' — (default) The categories are sorted by order of increasing values of odds, defined as the ratio of “Good” to “Bad” observations, for the given category.

      • 'Totals' — The categories are sorted by order of increasing values of total number of observations (“Good” plus “Bad”).

      • 'None' — No sorting is applied. The existing order of the categories is unchanged before applying the algorithm. (The existing order of the categories can be seen in the category grouping optional output from bininfo.)

      For more information, see Sort Categories

  • For Merge algorithm:

    • {'InitialNumBins',n} — Specifies an integer that determines the number (n >0) of bins that the predictor is initially binned into before merging. Valid for numeric predictors only. Default is 50.

    • {'Measure',MeasureName} — Specifies the measure where 'MeasureName' is one of the following:'Chi2' (default), 'Gini', 'InfoValue', or 'Entropy'.

    • {'MinNumBins',n} — Specifies the minimum number n (n>=2) of bins that result from merging. The default value is 2.

    • {'MaxNumBins',n} — Specifies the maximum number n (n>=2) of bins that result from merging. The default value is 5.

    • {'Tolerance',n} — Specifies the minimum threshold below which merging happens for the information value and entropy statistics. Valid values are in the interval (0.1). Default is 1e3.

    • {'Significance',n} — Significance level threshold for the chi-square statistic, below which merging happens. Values are in the interval [0,1]. Default is 0.9 (90% significance level).

    • {'SortCategories','SortOption'} — Used for categorical predictors only. Used to determine how the predictor categories are sorted as a preprocessing step before applying the algorithm. The values of 'SortOption' are:

      • 'Goods' — The categories are sorted by order of increasing values of “Good.”

      • 'Bads' — The categories are sorted by order of increasing values of “Bad.”

      • 'Odds' — (default) The categories are sorted by order of increasing values of odds, defined as the ratio of “Good” to “Bad” observations, for the given category.

      • 'Totals' — The categories are sorted by order of increasing values of total number of observations (“Good” plus “Bad”).

      • 'None' — No sorting is applied. The existing order of the categories is unchanged before applying the algorithm. (The existing order of the categories can be seen in the category grouping optional output from bininfo.)

      For more information, see Sort Categories

  • For EqualFrequency algorithm:

    • {'NumBins',n} — Specifies the desired number (n) of bins. The default is {'NumBins',5} and the number of bins must be a positive number.

    • {'SortCategories','SortOption'} — Used for categorical predictors only. Used to determine how the predictor categories are sorted as a preprocessing step before applying the algorithm. The values of 'SortOption' are:

      • 'Odds' — (default) The categories are sorted by order of increasing values of odds, defined as the ratio of “Good” to “Bad” observations, for the given category.

      • 'Goods' — The categories are sorted by order of increasing values of “Good.”

      • 'Bads' — The categories are sorted by order of increasing values of “Bad.”

      • 'Totals' — The categories are sorted by order of increasing values of total number of observations (“Good” plus “Bad”).

      • 'None' — No sorting is applied. The existing order of the categories is unchanged before applying the algorithm. (The existing order of the categories can be seen in the category grouping optional output from bininfo.)

      For more information, see Sort Categories

  • For EqualWidth algorithm:

    • {'NumBins',n} — Specifies the desired number (n) of bins. The default is {'NumBins',5} and the number of bins must be a positive number.

    • {'SortCategories','SortOption'} — Used for categorical predictors only. Used to determine how the predictor categories are sorted as a preprocessing step before applying the algorithm. The values of 'SortOption' are:

      • 'Odds' — (default) The categories are sorted by order of increasing values of odds, defined as the ratio of “Good” to “Bad” observations, for the given category.

      • 'Goods' — The categories are sorted by order of increasing values of “Good.”

      • 'Bads' — The categories are sorted by order of increasing values of “Bad.”

      • 'Totals' — The categories are sorted by order of increasing values of total number of observations (“Good” plus “Bad”).

      • 'None' — No sorting is applied. The existing order of the categories is unchanged before applying the algorithm. (The existing order of the categories can be seen in the category grouping optional output from bininfo.)

      For more information, see Sort Categories

Example: sc = autobinning(sc,'CustAge','Algorithm','Monotone','AlgorithmOptions',{'Trend','Increasing'})

Data Types: cell

Indicator to display the information on status of the binning process at command line, specified as the comma-separated pair consisting of 'Display' and a character vector with a value of 'On' or 'Off'.

Data Types: char

Output Arguments

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Credit scorecard model, returned as an updated creditscorecard object containing the automatically determined binning maps or rules (cut points or category groupings) for one or more predictors. For more information on using the creditscorecard object, see creditscorecard.

Note

If you have previously used the modifybins function to manually modify bins, these changes are lost when running autobinning because all the data is automatically binned based on internal autobinning rules.

More About

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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

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Topics