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bininfo

Return predictor’s bin information

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Syntax

bi = bininfo(sc,PredictorName)
bi = bininfo(___、名称、值)
[bi,bm] = bininfo(sc,PredictorName,Name,Value)
[bi,bm,mv] = bininfo(sc,PredictorName,Name,Value)

Description

example

bi= bininfo(sc,PredictorName)returns information at bin level, such as frequencies of “Good,” “Bad,” and bin statistics for the predictor specified inPredictorName.

example

bi= bininfo(___,Name,Value)adds optional name-value arguments.

example

[bi,bm] = bininfo(sc,PredictorName,Name,Value)adds optional name-value arguments.bininfoalso optionally returns the binning map (bm) or bin rules in the form of a vector of cut points for numeric predictors, or a table of category groupings for categorical predictors.

example

[bi,bm,mv] = bininfo(sc,PredictorName,Name,Value)returns information at bin level, such as frequencies of “Good,” “Bad," and bin statistics for the predictor specified inPredictorNameusing optional name-value pair arguments.bininfooptionally returns the binning map or bin rules in the form of a vector of cut points for numeric predictors, or a table of category groupings for categorical predictors. In addition, optional name-value pair argumentsmvreturns a numeric array containing the minimum and maximum values, as set (or defined) by the user. Themvoutput argument is set to an empty array for categorical predictors.

Examples

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Open Live Script

Create acreditscorecardobject using theCreditCardData.matfile to load thedata(using a dataset from Refaat 2011).

loadCreditCardDatasc = creditscorecard(data);

Display bin information for the categorical predictorResStatus.

bi = bininfo(sc,'ResStatus')
bi=4×6 tableBin 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

This example uses:

Open Live Script

使用theCreditCardData.matfile to load the data (dataWeights) that contains a column (RowWeights) for the weights (using a dataset from Refaat 2011).

loadCreditCardData

Create acreditscorecard使用可选的对象名称-值对的论点for'WeightsVar'.

sc = creditscorecard(dataWeights,'WeightsVar','RowWeights')
sc = creditscorecard with properties: GoodLabel: 0 ResponseVar: 'status' WeightsVar: 'RowWeights' VarNames: {1x12 cell} NumericPredictors: {1x7 cell} CategoricalPredictors: {'ResStatus' 'EmpStatus' 'OtherCC'} BinMissingData: 0 IDVar: '' PredictorVars: {1x10 cell} Data: [1200x12 table]

Display bin information for the numerical predictor'CustIncome'. When the optional name-value pair argument'WeightsVar'is used to specify observation (sample) weights, thebitable contains weighted counts.

bi = bininfo(sc,'CustIncome'); bi(1:10,:)
ans=10×6 tableBin Good Bad Odds WOE InfoValue _________ _______ _______ _______ ________ __________ {'18000'} 0.94515 1.496 0.63179 -1.1667 0.0059198 {'19000'} 0.47588 0.80569 0.59065 -1.2341 0.0034716 {'20000'} 2.1671 1.4636 1.4806 -0.31509 0.00061392 {'21000'} 3.2522 0.88064 3.693 0.59889 0.0021303 {'22000'} 1.5438 1.2714 1.2142 -0.51346 0.0012913 {'23000'} 1.787 2.7529 0.64913 -1.1397 0.010509 {'24000'} 3.4111 2.2538 1.5135 -0.29311 0.00082663 {'25000'} 2.2333 6.1383 0.36383 -1.7186 0.042642 {'26000'} 2.1246 4.4754 0.47474 -1.4525 0.024526 {'27000'} 3.1058 3.528 0.88032 -0.83501 0.0082144
Open Live Script

Create acreditscorecardobject using theCreditCardData.matfile to load thedata(using a dataset from Refaat 2011).

loadCreditCardDatasc = creditscorecard(data);

Display customized bin information for the categorical predictorResStatus, keeping only theWOEcolumn. The Weight-of-Evidence (WOE) is defined bin by bin, but there is no concept of "total WOE", therefore the last element in the'Totals'row is set toNaN.

bi = bininfo(sc,'ResStatus','Statistics','WOE'); disp(bi)
Bin Good Bad WOE ______________ ____ ___ _________ {'Home Owner'} 365 177 0.019329 {'Tenant' } 307 167 -0.095564 {'Other' } 131 53 0.20049 {'Totals' } 803 397 NaN

Display customized bin information for the categorical predictorResStatus, keeping only theOddsandWOEcolumns, without theTotalsrow.

bi = bininfo(sc,'ResStatus','Statistics',{'Odds','WOE'},'Totals','Off'); disp(bi)
Bin Good Bad Odds WOE ______________ ____ ___ ______ _________ {'Home Owner'} 365 177 2.0621 0.019329 {'Tenant' } 307 167 1.8383 -0.095564 {'Other' } 131 53 2.4717 0.20049

Display information value, entropy, Gini, and chi square statistics. For more information on these statistics, seeStatistics for a Credit Scorecard.

For information value, entropy and Gini, the value reported at a bin level is the contribution of the bin to the total value. The total information value, entropy, and Gini measures are in the'Totals'row.

For chi square, if there areNbins, the firstN-1values in the'Chi2'column report pairwise chi square statistics for contiguous bins. For example, this pairwise measure is also used by the'Merge'algorithm inautobinningto determine if two contiguous bins should be merged. In this example, the first value in the'Chi2'column (1.0331) is the chi square statistic of bins 1 and 2 ('Home Owner'and'Tenant'), and the second value in the column (2.5103) is the chi square statistic of bins 2 and 3 ('Tenant'and'Other'). There are no more pairwise chi square values to compute in this example, so the third element of the'Chi2'column is set toNaN. The chi square value reported in the'Totals'row is the chi square statistic computed over all bins.

bi = bininfo(sc,'ResStatus','Statistics',{'InfoValue','Entropy','Gini','Chi2'}); disp(bi)
Bin Good Bad InfoValue Entropy Gini Chi2 ______________ ____ ___ _________ _______ _______ ______ {'Home Owner'} 365 177 0.0001682 0.91138 0.43984 1.0331 {'Tenant' } 307 167 0.0036638 0.93612 0.45638 2.5103 {'Other' } 131 53 0.0059418 0.86618 0.41015 NaN {'Totals' } 803 397 0.0097738 0.91422 0.44182 2.5549
Open Live Script

Create acreditscorecardobject using theCreditCardData.matfile to load thedata(using a dataset from Refaat 2011).

loadCreditCardDatasc = creditscorecard(data);

装箱米ap or rules for categorical data are summarized in a "category grouping" table, returned as an optional output. By default, each category is placed in a separate bin. Here is the information for the predictorResStatus.

[bi,cg] = bininfo(sc,'ResStatus')
bi=4×6 tableBin 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
cg=3×2 tableCategory BinNumber ______________ _________ {'Home Owner'} 1 {'Tenant' } 2 {'Other' } 3

To group categoriesTenantandOther, modify the category grouping tablecgso that the bin number forOtheris the same as the bin number forTenant. Then use themodifybinsfunction to update the scorecard.

cg.BinNumber(3) = 2; sc = modifybins(sc,'ResStatus','CatGrouping',cg);

显示更新本信息。The bin labels have been updated and that the bin membership information is contained in the category groupingcg.

[bi,cg] = bininfo(sc,'ResStatus')
bi=3×6 tableBin Good Bad Odds WOE InfoValue __________ ____ ___ ______ _________ __________ {'Group1'} 365 177 2.0621 0.019329 0.0001682 {'Group2'} 438 220 1.9909 -0.015827 0.00013772 {'Totals'} 803 397 2.0227 NaN 0.00030592
cg=3×2 tableCategory BinNumber ______________ _________ {'Home Owner'} 1 {'Tenant' } 2 {'Other' } 2
Open Live Script

Create acreditscorecardobject using theCreditCardData.matfile to load thedata(using a dataset from Refaat 2011).

loadCreditCardDatasc = creditscorecard(data);

The predictorCustIncomeis numeric. By default, each value of the predictor is placed in a separate bin.

bi = bininfo(sc,'CustIncome')
bi=46×6 tableBin Good Bad Odds WOE InfoValue _________ ____ ___ _______ _________ __________ {'18000'} 2 3 0.66667 -1.1099 0.0056227 {'19000'} 1 2 0.5 -1.3976 0.0053002 {'20000'} 4 2 2 -0.011271 6.3641e-07 {'21000'} 6 3 2 -0.011271 9.5462e-07 {'22000'} 4 2 2 -0.011271 6.3641e-07 {'23000'} 4 4 1 -0.70442 0.0035885 {'24000'} 5 5 1 -0.70442 0.0044856 {'25000'} 4 9 0.44444 -1.5153 0.026805 {'26000'} 4 11 0.36364 -1.716 0.038999 {'27000'} 6 6 1 -0.70442 0.0053827 {'28000'} 13 11 1.1818 -0.53736 0.0061896 {'29000'} 11 10 1.1 -0.60911 0.0069988 {'30000'} 18 16 1.125 -0.58664 0.010493 {'31000'} 24 8 3 0.39419 0.0038382 {'32000'} 21 15 1.4 -0.36795 0.0042797 {'33000'} 35 19 1.8421 -0.093509 0.00039951 ⋮

Reduce the number of bins using theautobinningfunction (themodifybinsfunction can also be used).

sc = autobinning (sc,'CustIncome');

显示更新本信息。装箱米ap or rules for numeric data are summarized as "cut points," returned as an optional output (cp).

[bi,cp] = bininfo(sc,'CustIncome')
bi=8×6 tableBin 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
cp =6×129000 33000 35000 40000 42000 47000

Manually remove the second cut point (the boundary between the second and third bins) to merge bins two and three. Use themodifybinsfunction to update the scorecard.

cp(2) = []; sc = modifybins(sc,'CustIncome','CutPoints',cp,'MinValue',0);

显示更新本信息。

[bi,cp,mv] = bininfo(sc,'CustIncome')
bi=7×6 tableBin Good Bad Odds WOE InfoValue _________________ ____ ___ _______ _________ __________ {'[0,29000)' } 53 58 0.91379 -0.79457 0.06364 {'[29000,35000)'} 142 85 1.6706 -0.19124 0.0071274 {'[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 0.12043
cp =5×129000 35000 40000 42000 47000
mv =1×20 Inf

Note, it is recommended to avoid having bins with frequencies of zero because they lead to infinite or undefined (NaN) statistics. Use themodifybinsorautobinningfunctions to modify bins.

Open Live Script

Create acreditscorecardobject using theCreditCardData.matfile to load thedataMissingwith missing values.

loadCreditCardData.mathead(dataMissing,5)
CustID CustAge TmAtAddress ResStatus EmpStatus CustIncome TmWBank OtherCC AMBalance UtilRate status ______ _______ ___________ ___________ _________ __________ _______ _______ _________ ________ ______ 1 53 62  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

使用creditscorecardwith the name-value argument'BinMissingData'set totrueto bin the missing data in a separate bin.

sc = creditscorecard(dataMissing,'IDVar','CustID','BinMissingData',真正的);sc = autobinning (sc);disp (sc)
creditscorecard with properties: GoodLabel: 0 ResponseVar: 'status' WeightsVar: '' VarNames: {1x11 cell} NumericPredictors: {1x6 cell} CategoricalPredictors: {'ResStatus' 'EmpStatus' 'OtherCC'} BinMissingData: 1 IDVar: 'CustID' PredictorVars: {1x9 cell} Data: [1200x11 table]

Display bin information for numeric data for'CustAge'that includes missing data in a separate bin labelled.

bi = 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 {''} 19 11 1.7273 -0.15787 0.00063885 {'Totals' } 803 397 2.0227 NaN 0.087112
plotbins(sc,'CustAge')

{

Display bin information for categorical data for'ResStatus'that includes missing data in a separate bin labelled.

[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 {'' } 27 13 2.0769 0.026469 2.3248e-05 {'Totals' } 803 397 2.0227 NaN 0.0092627
disp(cg)
Category BinNumber ______________ _________ {'Tenant' } 1 {'Home Owner'} 2 {'Other' } 3

Note that the category grouping table does not includebecause this is a reserved bin and users cannot interact directly with thebin.

plotbins(sc,'ResStatus')

{

Input Arguments

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Credit scorecard model, specified as acreditscorecard对象。使用creditscorecardto create acreditscorecard对象。

Predictor name, specified using a character vector containing the name of the predictor.PredictorNameis case-sensitive.

Data Types:char

Name-Value Arguments

Specify optional pairs of arguments asName1=Value1,...,NameN=ValueN, whereNameis the argument name andValueis 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 encloseName在报价。

Example:bi = bininfo(sc, PredictorName,'Statistics','WOE','Totals','On')

List of statistics to include in the bin information, specified as the comma-separated pair consisting of'Statistics'and a character vector or a cell array of character vectors. For more information, seeStatistics for a Credit Scorecard. Possible values are:

  • 'Odds'— Odds information is the ratio of “Goods” over “Bads.”

  • 'WOE'— Weight of Evidence. The WOE Statistic measures the deviation between the distribution of “Goods” and “Bads.”

  • 'InfoValue'— Information value. Closely tied to the WOE, it is a statistic used to determine how strong a predictor is to use in the fitting model. It measures how strong the deviation is between the distributions of “Goods” and “Bads.” However, bins with only “Good” or only “Bad” observations do lead to an infinite Information Value. Consider modifying the bins in those cases by usingmodifybinsorautobinning.

  • 'Entropy'— Entropy is a measure of unpredictability contained in the bins. The more the number of “Goods” and “Bads” differ within the bins, the lower the entropy.

  • 'Gini'— Measure of statistical dispersion or inequality within a sample of data.

  • 'Chi2'— Measure of statistical difference and independence between groups.

Note

Avoid having bins with frequencies of zero because they lead to infinite or undefined (NaN) statistics. Usemodifybinsorautobinningto modify bins.

Data Types:char|cell

Indicator to include a row of totals at the bottom of the information table, specified as the comma-separated pair consisting of'Totals'and a character vector with valuesOnorOff.

Data Types:char

Output Arguments

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Bin information, returned as a table. The bin information table contains one row per bin and a row of totals. The columns contain bin descriptions, frequencies of “Good” and “Bad,” and bin statistics. Avoid having bins with frequencies of zero because they lead to infinite or undefined (NaN) statistics. Usemodifybinsorautobinningto modify bins.

Note

When creating thecreditscorecardobject withcreditscorecard, if the optional name-value pair argumentWeightsVarwas used to specify observation (sample) weights, then thebitable contains weighted counts.

Binning map or rules, returned as a vector of cut points for numeric predictors, or a table of category groupings for categorical predictors. For more information, seemodifybins.

Binning minimum and maximum values (as set or defined by the user), returned as a numeric array. Themvoutput argument is set to an empty array for categorical predictors.

More About

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Statistics for a Credit Scorecard

Weight of Evidence (WOE) is a measure of the difference of the distribution of “Goods” and “Bads” within a bin.

Suppose the predictor's data takes onMpossible values b1, ..., bM. For binned data,Mis a small number. The response takes on two values, “Good” and “Bad.” The frequency table of the data is given by:

Good Bad Total
b1: n11 n12 n1
b2: n21 n22 n2
bM: nM1 nM2 nM
Total: nGood nBad nTotal

The Weight of Evidence (WOE) is defined for each data valuebias

WOE(i) = log((ni1/nGood)/(ni2/nBad)).

If you define

pGood(i) = ni1/nGood, pBad(i) = ni2/nBad

thenpGood(i) is the proportion of “Good” observations that take on the valuebi, and similarly forpBad(i). In other words,pGood(i) gives the distribution of good observations over theMobserved values of the predictor, and similarly forpBad(i). With this, an equivalent formula for the WOE is

WOE(i) = log(pGood(i)/pBad(i)).
Using the same frequency table, the odds for rowiare defined as
Odds(i) = ni1 / ni2,
and the odds for the sample are defined as
OddsTotal = nGood / nBad.

For each rowi, you can also compute its contribution to the total Information Value, given by

InfoValue(i) = (pGood(i) - pBad(i)) * WOE(i),

and the total Information Value is simply the sum of all theInfoValuel(i) terms. (Anansumis returned to discard contributions from rows with no observations at all.)

Likewise, for each rowi, we can compute its contribution to the total Entropy, given by

Entropy(i) = -1/log(2)*(ni1/ni*log(ni1/ni)+ni2/ni*log(ni2/ni),
and the total Entropy is simply the weighted sum of the row entropies,
Entropy = sum(ni/nTotal * Entropy(i)), i = 1...M.

Chi2 is computed pairwise for each pair of bins and measures the statistical difference between two groups when splitting or merging bins and is defined as:

Chi2 = sum(sum((Aij - Eij)^2/Eij , j=1..k), i=m,m+1).
For more information on splitting and merging bins, seeSplitandMerge.

Gini ratio is a measure of the parent node, that is, of the given bins/categories prior to splitting or merging. The Gini ratio is defined as:

Gr = 1-G_hat/Gp
G_hatis the weighted Gini measure for the current split or merge:
G_hat = Sum((nj/N) * Gj, j=1..m).
For more information on splitting and merging bins, seeSplitandMerge.

Usingbininfowith Weights

When observation weights are defined using the optionalWeightsVarargument when creating acreditscorecardobject, instead of counting the rows that are good or bad in each bin, thebininfofunction accumulates the weight of the rows that are good or bad in each bin.

The “frequencies” reported are no longer the basic “count” of rows, but the “cumulative weight” of the rows that are good or bad and fall in a particular bin. Once these “weighted frequencies” are known, all other relevant statistics (Good,Bad,Odds,WOE, andInfoValue) are computed with the usual formulas. For more information, seeCredit Scorecard Modeling Using Observation Weights.

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

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