plotbins
Plot histogram counts for predictor variables
Syntax
Description
plotbins(
plots histogram counts for given predictor variables. When a predictor’s bins
are modified using sc
,PredictorName
)modifybins
or autobinning
, rerun
plotbins
to update the figure to reflect the change.
returns a handle to the figure. hFigure
= plotbins(sc
,PredictorName
)plotbins
plots histogram
counts for given predictor variables. When a predictor’s bins are modified using
modifybins
or autobinning
, rerun
plotbins
to update the figure to reflect the
change.
returns a handle to the figure. hFigure
= plotbins(___,Name,Value
)plotbins
plots histogram
counts for given predictor variables using optional name-value pair arguments.
When a predictor’s bins are modified using modifybins
or autobinning
, rerun
plotbins
to update the figure to reflect the
change.
Examples
Plot a Histogram for Bin Information
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 PredictorName
input argument for CustIncome
using the defaults for the algorithm Monotone
.
sc = autobinning(sc, 'CustIncome')
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: 0 IDVar: '' PredictorVars: {'CustID' 'CustAge' 'TmAtAddress' 'ResStatus' 'EmpStatus' 'CustIncome' 'TmWBank' 'OtherCC' 'AMBalance' 'UtilRate'} Data: [1200x11 table]
Use bininfo
to display the autobinned data.
[bi, cp] = 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
cp = 6×1
29000
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 the modifybins
function to update the scorecard and then display updated bin information.
cp(2) = []; sc = modifybins(sc,'CustIncome','CutPoints',cp); bi = bininfo(sc,'CustIncome')
bi=7×6 table
Bin Good Bad Odds WOE InfoValue
_________________ ____ ___ _______ _________ __________
{'[-Inf,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
Plot the histogram count for updated bin information for the PredictorName
called CustIncome
.
plotbins(sc,'CustIncome')
Plot a Histogram for Bin Information Using 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 PredictorName
input argument for CustIncome
using the defaults for the algorithm Monotone
.
sc = autobinning(sc, 'CustIncome')
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: 0 IDVar: '' PredictorVars: {'CustID' 'CustAge' 'TmAtAddress' 'ResStatus' 'EmpStatus' 'CustIncome' 'TmWBank' 'OtherCC' 'AMBalance' 'UtilRate'} Data: [1200x11 table]
Use bininfo
to display the autobinned data.
[bi, cp] = 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
cp = 6×1
29000
33000
35000
40000
42000
47000
Plot the bin information for CustIncome
without the Weight of Evidence (WOE) line and without a legend by setting the 'WOE'
and 'Legend'
name-value arguments to 'Off'
. Also, set the 'BinText'
name-value pair argument to 'PercentRows'
to show as text over the plot bars for the proportion of "Good" and "Bad" within each bin, that is, the probability of "Good" and "Bad" within each bin.
plotbins(sc,'CustIncome','WOE','Off','Legend','Off','BinText','PercentRows')
Plot Bin Information When Using Missing Data
Create a creditscorecard
object using the CreditCardData.mat
file to load the data
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 or categorical data in a separate bin.
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')
Input Arguments
sc
— Credit scorecard model
creditscorecard
object
Credit scorecard model, specified as a
creditscorecard
object. Use creditscorecard
to create
a creditscorecard
object.
PredictorName
— Name of one or more predictors to plot
character vector with predictor name | cell array of character vectors with predictor names
Name of one or more predictors to plot, specified using a character vector or cell array of character vectors containing one or more names of the predictors.
Data Types: char
| cell
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
Example: plotbins(sc,PredictorName,'BinText','Count','WOE','On')
BinText
— Information to display on top of plotted bin counts
'None'
(default) | character vector with values 'None'
,
'Count'
, 'PercentRows'
,
'PercentCols'
,
'PercentTotal'
Information to display on top of plotted bin counts, specified as
the comma-separated pair consisting of 'BinText'
and
a character vector with values:
None
— No text is displayed on top of the bins.Count
— For each bin, displays the count for “Good” and “Bad.”PercentRows
— For each bin, displays the count for “Good” and “Bad” as a percentage of the number of observations in the bin.PercentCols
— For each bin, displays the count for “Good” and “Bad” as a percentage of the total “Good” and total “Bad” in the entire sample.PercentTotal
— For each bin, displays the count for “Good” and “Bad” as a percentage of the total number of observations in the entire sample.
Data Types: char
WOE
— Indicator for Weight of Evidence (WOE)
'On'
(default) | character vector with values 'On'
,
'Off'
Indicator for Weight of Evidence (WOE) line, specified as the
comma-separated pair consisting of 'WOE'
and a
character vector with values On
or
Off
. When set to On
, the WOE
line is plotted on top of the histogram.
Data Types: char
Legend
— Indicator for legend on plot
'On'
(default) | character vector with values 'On'
,
'Off'
Indicator for legend on the plot, specified as the comma-separated
pair consisting of 'Legend'
and a character vector
with values On
or Off
.
Data Types: char
Output Arguments
hFigure
— Figure handle for histogram plot for predictor variables
figure object
Figure handle for histogram plot for predictor variables, returned as
figure object or array of figure objects if more than one
PredictorName
is specified as an input.
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
| bindata
| modifybins
| fitmodel
| displaypoints
| formatpoints
| score
| setmodel
| probdefault
| validatemodel
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