| Title: | Supervised Weight of Evidence Binning of Numeric Variables and Factors |
|---|---|
| Description: | Implements an automated binning of numeric variables and factors with respect to a dichotomous target variable. Two approaches are provided: An implementation of fine and coarse classing that merges granular classes and levels step by step. And a tree-like approach that iteratively segments the initial bins via binary splits. Both procedures merge, respectively split, bins based on similar weight of evidence (WOE) values and stop via an information value (IV) based criteria. The package can be used with single variables or an entire data frame. It provides flexible tools for exploring different binning solutions and for deploying them to (new) data. |
| Authors: | Thilo Eichenberg |
| Maintainer: | Thilo Eichenberg <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.6 |
| Built: | 2024-12-04 07:25:49 UTC |
| Source: | CRAN |
Credit data that classifies debtors described by a set of attributes as good or bad credit risks. See source link below for detailed information.
data(germancredit)data(germancredit)
A data frame with 21 variables (numeric and factors) and 1000 observations.
https://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data)
# Load German credit data and create subset data(germancredit) df <- germancredit[, c('creditability', 'credit.amount', 'duration.in.month', 'savings.account.and.bonds', 'purpose')] # Display structure of the subset (data frame) str(df)# Load German credit data and create subset data(germancredit) df <- germancredit[, c('creditability', 'credit.amount', 'duration.in.month', 'savings.account.and.bonds', 'purpose')] # Display structure of the subset (data frame) str(df)
woe.binning generates a supervised fine and coarse classing of numeric
variables and factors with respect to a dichotomous target variable. Its parameters
provide flexibility in finding a binning that fits specific data characteristics
and practical needs.
woe.binning(df, target.var, pred.var, min.perc.total, min.perc.class, stop.limit, abbrev.fact.levels, event.class)woe.binning(df, target.var, pred.var, min.perc.total, min.perc.class, stop.limit, abbrev.fact.levels, event.class)
df |
Name of data frame with input data. |
target.var |
Name of dichotomous target variable in quotes. Only target variables with two distinct values (e.g. 0, 1 or “Y”, “N”) are accepted; cases with NAs in the target variable will be ignored. |
pred.var |
Name of predictor variable(s) to be binned in quotes. A single variable name can be provided, e.g. “varname1”, or a list of variable names, e.g. c(“varname1”, “varname2”). Alternatively one can repeat the name of the input data frame; the function will be applied to all its variables apart from the target variable then. Numeric variables and factors are supported and may contain NAs. |
min.perc.total |
For numeric variables this parameter defines the number of initial classes before any merging is applied. For example min.perc.total=0.05 (5%) will result in 20 initial classes. For factors the original levels with a percentage below this limit are collected in a ‘miscellaneous’ level before the merging based on the min.perc.class and on the WOE starts. Increasing the min.perc.total parameter will avoid sparse bins. Accepted range: 0.0001-0.2; default: 0.05. |
min.perc.class |
If a column percentage of one of the target classes within a bin is below this limit (e.g. below 0.01=1%) then the respective bin will be joined with others. In case of numeric variables adjacent predictor classes are merged. For factors respective levels (including sparse NAs) are assigned to a ‘miscellaneous’ level. Setting min.perc.class>0 may provide more reliable WOE values. Accepted range: 0-0.2; default: 0, i.e. no merging with respect to sparse target classes is applied. |
stop.limit |
Stops WOE based merging of the predictor's classes/levels in case the resulting information value (IV) decreases more than x% (e.g. 0.05 = 5%) compared to the preceding binning step. stop.limit=0 will skip any WOE based merging. Increasing the stop.limit will simplify the binning solution and may avoid overfitting. Accepted range: 0-0.5; default: 0.1. |
abbrev.fact.levels |
Abbreviates the names of new (merged) factor levels via the base R
|
event.class |
Optional parameter for specifying the class of the target event. This class typically indicates a negative event like a loan default or a disease. Use integers (e.g. 1) or characters in quotes (e.g. “bad”). This class will be represented by negative WOE values then. |
woe.binning generates an object containing the information necessary
for studying and applying the realized binning solution. When saved
it can be used with the functions woe.binning.plot, woe.binning.table
and woe.binning.deploy.
Numeric variables (continuous and ordinal) are binned by merging initial classes with similar frequencies. The number of initial bins results from the min.perc.total parameter: min.perc.total will result in trunc(1/min.perc.total) initial bins, whereby trunc is needed to guarantee bins with similar frequencies. For example min.perc.total=0.07 will cause trunc(14.3)=14 initial classes. Next, if min.perc.class>0, bins with sparse target classes will be merged with the next upper bin, and in case of the last bin with the next lower one. NAs have their own bin and will not be merged with others. Finally nearby bins with most similar weight of evidence (WOE) values are joined step by step until the information value (IV) decreases more than specified by a percentage value (stop.limit parameter) or until two bins are reached.
Factors (categorical variables) are binned by merging factor levels. As a start sparse levels (defined via the min.perc.total and min.perc.class parameters) are merged to a ‘miscellaneous’ level: if possible, respective levels (including sparse NAs) are bundled as ‘misc. level pos.’ (associated with positive WOE values), respectively as ‘misc. level neg.’ (associated with negative WOE values). In case a misc. level contains only NAs it will be named ‘Missing’. Afterwards levels with similar WOE values are joined step by step until the information value (IV) decreases more than specified by a percentage value (stop.limit parameter) or until two bins are reached.
In case the crosstab of the bins with the target classes contains frequencies = 0 the column percentages are adjusted to be able to compute the WOE and IV values: the offset 0.0001 (=0.01%) is added to each column percentage cell and the column percentages are recomputed then. This allows considering bins associated with one target class only, but may cause extreme WOE values for these bins. If a correction is not appropriate choose min.perc.class>0; bins with sparse target classes will be merged then before computing any WOE or IV value.
Cases with NAs in the target variable will be ignored. For predictor variables the following applies: in case NAs already occurred when generating the binning solution the code ‘Missing’ is displayed and a corresponding WOE value can be computed. (Note that factor NAs may be joined with other sparse levels to a ‘miscellaneous’ level - see above; only this ‘miscellaneous’ level will be displayed then.) In case NAs occur in the deployment scenario only ‘Missing’ is displayed for numeric variables and ‘unknown’ for factors; and the corresponding WOE values will be NA then, as well.
Other binning functions: woe.tree.binning
# Load German credit data and create subset data(germancredit) df <- germancredit[, c('creditability', 'credit.amount', 'duration.in.month', 'savings.account.and.bonds', 'purpose')] # Bin a single numeric variable binning <- woe.binning(df, 'creditability', 'duration.in.month', min.perc.total=0.05, min.perc.class=0.01, stop.limit=0.1, event.class='bad') # Bin a single factor binning <- woe.binning(df, 'creditability', 'purpose', min.perc.total=0.05, min.perc.class=0, stop.limit=0.1, abbrev.fact.levels=50, event.class='bad') # Bin two variables (one numeric and one factor) # with default parameter settings binning <- woe.binning(df, 'creditability', c('credit.amount','purpose')) # Bin all variables of the data frame (apart from the target variable) # with default parameter settings binning <- woe.binning(df, 'creditability', df)# Load German credit data and create subset data(germancredit) df <- germancredit[, c('creditability', 'credit.amount', 'duration.in.month', 'savings.account.and.bonds', 'purpose')] # Bin a single numeric variable binning <- woe.binning(df, 'creditability', 'duration.in.month', min.perc.total=0.05, min.perc.class=0.01, stop.limit=0.1, event.class='bad') # Bin a single factor binning <- woe.binning(df, 'creditability', 'purpose', min.perc.total=0.05, min.perc.class=0, stop.limit=0.1, abbrev.fact.levels=50, event.class='bad') # Bin two variables (one numeric and one factor) # with default parameter settings binning <- woe.binning(df, 'creditability', c('credit.amount','purpose')) # Bin all variables of the data frame (apart from the target variable) # with default parameter settings binning <- woe.binning(df, 'creditability', df)
woe.binning.deploy applies the binning solution generated and saved via the woe.binning
or woe.tree.binning function to (new) data.
woe.binning.deploy(df, binning, min.iv.total, add.woe.or.dum.var)woe.binning.deploy(df, binning, min.iv.total, add.woe.or.dum.var)
df |
Name of the data frame the binning solution - that was generated via the function |
binning |
Binning information generated from the |
min.iv.total |
If the IV total value of a binned variable falls below this limit (e.g. 0.1) it will not be added to the data. Just omit this parameter in case you would like to add all binned variables (default). |
add.woe.or.dum.var |
add.woe.or.dum.var=“woe” adds an additional variable with WOE scores
and =“dum” additional dummy variables for each (aggregated) level
of the binned variable. In case of dummy variables make sure that you have set
an appropriate abbrev.fact.levels parameter in the |
woe.binning.deploy applies the binning information that was generated from the woe.binning
or woe.tree.binning function to a data frame. In this data frame the names of the variables
to be binned need to be identical to the ones used with the woe.binning
or woe.tree.binning function. For each variable a binned version will be added.
Optionally a variable with associated weight of evidence (WOE) values or corresponding
dummy variables (one dummy variable for each final bin) are provided.
In case NAs already occurred during the woe.binning or woe.tree.binning binning process the code
‘Missing’ is displayed and a corresponding WOE value can be computed.
In case NAs only occur in the deployment scenario ‘Missing’ is
displayed for numeric variables and ‘unknown’ for factors; and
the corresponding WOE values will be NAs then, as well.
For factor levels that have not been provided in generating the
binning solution via the woe.binning or woe.tree.binning function a new factor
level ‘unknown’ is displayed and the corresponding WOE value will be NA.
# Load German credit data and create a subset data(germancredit) df <- germancredit[, c('creditability', 'credit.amount', 'duration.in.month', 'savings.account.and.bonds', 'purpose')] # Bin all variables of the data frame (apart from the target variable) # with default parameter settings binning <- woe.binning(df, 'creditability', df) # Deploy the binning solution to the data frame # (add all binned variables and corresponding WOE variables) df.with.binned.vars.added <- woe.binning.deploy(df, binning, add.woe.or.dum.var='woe') # Deploy the binning solution to the data frame # (add binned variables with IV>=0.1 and corresponding dummy variables) df.with.binned.vars.added <- woe.binning.deploy(df, binning, min.iv.total=0.1, add.woe.or.dum.var='dum')# Load German credit data and create a subset data(germancredit) df <- germancredit[, c('creditability', 'credit.amount', 'duration.in.month', 'savings.account.and.bonds', 'purpose')] # Bin all variables of the data frame (apart from the target variable) # with default parameter settings binning <- woe.binning(df, 'creditability', df) # Deploy the binning solution to the data frame # (add all binned variables and corresponding WOE variables) df.with.binned.vars.added <- woe.binning.deploy(df, binning, add.woe.or.dum.var='woe') # Deploy the binning solution to the data frame # (add binned variables with IV>=0.1 and corresponding dummy variables) df.with.binned.vars.added <- woe.binning.deploy(df, binning, min.iv.total=0.1, add.woe.or.dum.var='dum')
woe.binning.plot visualizes the binning solution generated and saved via the woe.binning
or woe.tree.binning function.
woe.binning.plot(binning, multiple.plots, plot.range)woe.binning.plot(binning, multiple.plots, plot.range)
binning |
Binning information generated from the |
multiple.plots |
In case the binning solution contains several predictor variables they will be visualized via multiple plots (max. four WOE plots per graph window). Use multiple.plots=FALSE to avoid this and to display single plots in separate windows. |
plot.range |
Range of variables that should be plotted in quotes. For example “1:10” will generate WOE plots and one IV plot for the ten variables with the highest IV values, “11:20” for the next ten variables and so on. Just omit this parameter to visualize all binned variables (default). |
For each predictor variable woe.binning.plot generates a weight of evidence
(WOE) plot. In case of multiple predictors an additional plot with variables ranked
via the information value (IV) will be displayed.
# Load German credit data data(germancredit) df <- germancredit # Bin all variables of the data frame (apart from the target variable) # with default parameter settings binning <- woe.binning(df, 'creditability', df) # Plot all binned variables as multiple plots woe.binning.plot(binning) # Plot only the first four binned variables with the highest IV value # as multiple plots woe.binning.plot(binning, plot.range='1:4') # Plot the binned variables in single plots woe.binning.plot(binning, multiple.plots=FALSE)# Load German credit data data(germancredit) df <- germancredit # Bin all variables of the data frame (apart from the target variable) # with default parameter settings binning <- woe.binning(df, 'creditability', df) # Plot all binned variables as multiple plots woe.binning.plot(binning) # Plot only the first four binned variables with the highest IV value # as multiple plots woe.binning.plot(binning, plot.range='1:4') # Plot the binned variables in single plots woe.binning.plot(binning, multiple.plots=FALSE)
woe.binning.table tabulates the binning solution generated and saved via the woe.binning
or woe.tree.binning function.
woe.binning.table(binning)woe.binning.table(binning)
binning |
Binning information generated from the |
For each predictor variable woe.binning.table generates a table (data frame).
This table contains the final bin labels, total counts, total distribution (column percentages),
counts for the first and the second target class, distribution of the first and the second target
class (column percentages), rate (row percentages) of the target event specified via the
event.class parameter in the woe.binning or woe.tree.binning function, as well as weight of evidence
(WOE) and information values (IV).
# Load German credit data and create a subset data(germancredit) df <- germancredit[, c('creditability', 'credit.amount', 'duration.in.month', 'savings.account.and.bonds', 'purpose')] # Bin all variables of the data frame (apart from the target variable) # with default parameter settings binning <- woe.binning(df, 'creditability', df) # Tabulate the binned variables tabulate.binning <- woe.binning.table(binning) tabulate.binning ## Not run: # Plot a layouted table (using the gridExtra library) for a specific # variable (in this example for the first binned variable # with the highest IV value) library(gridExtra) grid.table(tabulate.binning[[1]], theme = ttheme_default(core=list(bg_params= list(fill=c(rep(c('grey95','grey90'), length.out=nrow(tabulate.binning[[1]])-1), '#BCC7BD')),fg_params=list(cex=0.8)), colhead=list(fg_params=list(cex=0.8))), rows=NULL) ## End(Not run)# Load German credit data and create a subset data(germancredit) df <- germancredit[, c('creditability', 'credit.amount', 'duration.in.month', 'savings.account.and.bonds', 'purpose')] # Bin all variables of the data frame (apart from the target variable) # with default parameter settings binning <- woe.binning(df, 'creditability', df) # Tabulate the binned variables tabulate.binning <- woe.binning.table(binning) tabulate.binning ## Not run: # Plot a layouted table (using the gridExtra library) for a specific # variable (in this example for the first binned variable # with the highest IV value) library(gridExtra) grid.table(tabulate.binning[[1]], theme = ttheme_default(core=list(bg_params= list(fill=c(rep(c('grey95','grey90'), length.out=nrow(tabulate.binning[[1]])-1), '#BCC7BD')),fg_params=list(cex=0.8)), colhead=list(fg_params=list(cex=0.8))), rows=NULL) ## End(Not run)
woe.tree.binning generates a supervised tree-like segmentation of numeric variables
and factors with respect to a dichotomous target variable. Its parameters provide
flexibility in finding a binning that fits specific data characteristics and practical
needs.
woe.tree.binning(df, target.var, pred.var, min.perc.total, min.perc.class, stop.limit, abbrev.fact.levels, event.class)woe.tree.binning(df, target.var, pred.var, min.perc.total, min.perc.class, stop.limit, abbrev.fact.levels, event.class)
df |
Name of data frame with input data. |
target.var |
Name of dichotomous target variable in quotes. Only target variables with two distinct values (e.g. 0, 1 or “Y”, “N”) are accepted; cases with NAs in the target variable will be ignored. |
pred.var |
Name of predictor variable(s) to be binned in quotes. A single variable name can be provided, e.g. “varname1”, or a list of variable names, e.g. c(“varname1”, “varname2”). Alternatively one can repeat the name of the input data frame; the function will be applied to all its variables apart from the target variable then. Numeric variables and factors are supported and may contain NAs. |
min.perc.total |
For numeric variables this parameter defines the number of initial classes before any merging or tree-like splitting is applied. For example min.perc.total=0.05 (5%) will result in 20 initial classes. For factors the original levels with a percentage below this limit are collected in a ‘miscellaneous’ level before the merging based on the min.perc.class and the tree-like splitting based on the WOE values starts. Increasing the min.perc.total parameter will avoid sparse bins. Accepted range: 0.0001-0.2; default: 0.01. |
min.perc.class |
If a column percentage of one of the target classes within a bin is below this limit (e.g. below 0.01=1%) then the respective bin will be joined with others. In case of numeric variables adjacent predictor classes are merged. For factors respective levels (including sparse NAs) are assigned to a ‘miscellaneous’ level. Setting min.perc.class>0 may provide more reliable WOE values. Accepted range: 0-0.2; default: 0, i.e. no merging with respect to sparse target classes is applied. |
stop.limit |
Stops WOE based segmentation of the predictor's classes/levels in case the resulting information value (IV) increases less than x% (e.g. 0.05 = 5%) compared to the preceding binning step. Increasing the stop.limit will simplify the binning solution and may avoid overfitting. Accepted range: 0-0.5; default: 0.1. |
abbrev.fact.levels |
Abbreviates the names of new (merged) factor levels via the base R
|
event.class |
Optional parameter for specifying the class of the target event. This class typically indicates a negative event like a loan default or a disease. Use integers (e.g. 1) or characters in quotes (e.g. “bad”). This class will be represented by negative WOE values then. |
woe.tree.binning generates an object with the information necessary
for studying and applying the realized binning solution. When saved
it can be used with the functions woe.binning.plot, woe.binning.table
and woe.binning.deploy.
Numeric variables (continuous and ordinal) are binned beginning with initial classes with similar frequencies. The number of initial bins results from the min.perc.total parameter: min.perc.total will result in trunc(1/min.perc.total) initial bins, whereby trunc is needed to guarantee bins with similar frequencies. For example min.perc.total=0.07 will cause trunc(14.3)=14 initial classes. Next, if min.perc.class>0, bins with sparse target classes will be merged with the next upper bin, and in case of the last bin with the next lower one. NAs have their own bin and will not be merged with others. Finally the actual tree-like procedure starts: binary splits iteratively assign nearby classes with similar weight of evidence (WOE) values to segments in a way that maximizes the resulting information value (IV). The procedure stops when the IV increases less then specified by a percentage value (stop.limit parameter).
Factors (categorical variables) are binned via factor levels. As a start sparse levels (defined via the min.perc.total and min.perc.class parameters) are merged to a ‘miscellaneous’ level: if possible, respective levels (including sparse NAs) are bundled as ‘misc. level pos.’ (associated with positive WOE values), respectively as ‘misc. level neg.’ (associated with negative WOE values). In case a misc. level contains only NAs it will be named ‘Missing’. Afterwards the actual tree-like procedure starts: binary splits iteratively assign levels with similar WOE values to segments in a way that maximizes the resulting information value (IV). The procedure stops when the IV increases less then specified by a percentage value (stop.limit parameter).
In case the crosstab of the bins with the target classes contains frequencies = 0 the column percentages are adjusted to be able to compute the WOE and IV values: the offset 0.0001 (=0.01%) is added to each column percentage cell and the column percentages are recomputed then. This allows considering bins associated with one target class only, but may cause extreme WOE values for these bins. If a correction is not appropriate choose min.perc.class>0; bins with sparse target classes will be merged then before computing any WOE or IV value.
Cases with NAs in the target variable will be ignored. For predictor variables the following applies: in case NAs already occurred when generating the binning solution the code ‘Missing’ is displayed and a corresponding WOE value can be computed. (Note that factor NAs may be joined with other sparse levels to a ‘miscellaneous’ level - see above; only this ‘miscellaneous’ level will be displayed then.) In case NAs occur in the deployment scenario only ‘Missing’ is displayed for numeric variables and ‘unknown’ for factors; and the corresponding WOE values will be NA then, as well.
Other binning functions: woe.binning
# Load German credit data and create subset data(germancredit) df <- germancredit[, c('creditability', 'credit.amount', 'duration.in.month', 'savings.account.and.bonds', 'purpose')] # Bin a single numeric variable binning <- woe.tree.binning(df, 'creditability', 'duration.in.month', min.perc.total=0.01, min.perc.class=0.01, stop.limit=0.1, event.class='bad') # Bin a single factor binning <- woe.tree.binning(df, 'creditability', 'purpose', min.perc.total=0.05, min.perc.class=0, stop.limit=0.1, abbrev.fact.levels=50, event.class='bad') # Bin two variables (one numeric and one factor) # with default parameter settings binning <- woe.tree.binning(df, 'creditability', c('credit.amount','purpose')) # Bin all variables of the data frame (apart from the target variable) # with default parameter settings binning <- woe.tree.binning(df, 'creditability', df)# Load German credit data and create subset data(germancredit) df <- germancredit[, c('creditability', 'credit.amount', 'duration.in.month', 'savings.account.and.bonds', 'purpose')] # Bin a single numeric variable binning <- woe.tree.binning(df, 'creditability', 'duration.in.month', min.perc.total=0.01, min.perc.class=0.01, stop.limit=0.1, event.class='bad') # Bin a single factor binning <- woe.tree.binning(df, 'creditability', 'purpose', min.perc.total=0.05, min.perc.class=0, stop.limit=0.1, abbrev.fact.levels=50, event.class='bad') # Bin two variables (one numeric and one factor) # with default parameter settings binning <- woe.tree.binning(df, 'creditability', c('credit.amount','purpose')) # Bin all variables of the data frame (apart from the target variable) # with default parameter settings binning <- woe.tree.binning(df, 'creditability', df)
This package generates, visualizes, tabulates and deploys a supervised weight of evidence (WOE) binning of variables.
The package woeBinning automates the process of binning of numeric
variables and factors with respect to a dichotomous target variable.
Additionally, it visualizes the realized binning solution, tabulates it and
deploys it to (new) data. All functions can be used with single variables
or an entire data frame.
woe.binning generates a supervised fine and coarse classing of numeric variables and factors.
woe.tree.binning generates a supervised tree-like segmentation of numeric variables and factors.
woe.binning.plot visualizes the binning solution generated and saved via woe.binning or woe.tree.binning.
woe.binning.table tabulates the binning solution generated and saved via woe.binning or woe.tree.binning.
woe.binning.deploy deploys the binning solution generated and saved via woe.binning or woe.tree.binning to (new) data.
Siddiqi, N. 2006: Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. Hoboken, New Jersey: John Wiley & Sons.
Anderson, R. 2007: The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation. Oxford / New York: Oxford University Press.
# Load German credit data and create subset data(germancredit) df <- germancredit[, c('creditability', 'credit.amount', 'duration.in.month', 'savings.account.and.bonds', 'purpose')] # Bin all variables of the data frame (apart from the target variable) # with default parameter settings binning <- woe.binning(df, 'creditability', df) # Plot the binned variables woe.binning.plot(binning) # Tabulate the binned variables tabulate.binning <- woe.binning.table(binning) tabulate.binning # Deploy the binning solution to the data frame # (i.e. add binned variables and corresponding WOE variables) df.with.binned.vars.added <- woe.binning.deploy(df, binning, add.woe.or.dum.var='woe')# Load German credit data and create subset data(germancredit) df <- germancredit[, c('creditability', 'credit.amount', 'duration.in.month', 'savings.account.and.bonds', 'purpose')] # Bin all variables of the data frame (apart from the target variable) # with default parameter settings binning <- woe.binning(df, 'creditability', df) # Plot the binned variables woe.binning.plot(binning) # Tabulate the binned variables tabulate.binning <- woe.binning.table(binning) tabulate.binning # Deploy the binning solution to the data frame # (i.e. add binned variables and corresponding WOE variables) df.with.binned.vars.added <- woe.binning.deploy(df, binning, add.woe.or.dum.var='woe')