Description Usage Arguments Details Value See Also
preBinningFun
is often used to pre-binning with returning plots and a lots of details for variables filtering, and it has integrated 5 different binning methods(see details). Important notes: the calculated WOE value in this function is the opposite of the actual value.
1 2 | preBinningFun(mydata, binMethod = 1, p = 0.05, aliquots = 5,
mydict = NULL)
|
mydata |
A data frame of dataset consisting of only Xs and Y variables, the last column must be Y. All character x variables must be converted to factors in advance. |
binMethod |
An integer from 1 to 5, indicates 5 different binning methods(see details), default |
p |
A numeric, means percentage of records per bin, from 0 to 0.5, default |
aliquots |
An integer, specifies the number of bins for equal-frequency or equal-interval binning method, default |
mydict |
Optional, default |
binMethod=c(1,2,3,4,5)
, means:
1 means optimal binning, and equal-frequency binning is an alternative when optimal binning is not available.
2 means optimal binning, and equal-interval binning is an alternative when optimal binning is not available.
3 means equal-frequency binning.
4 means equal-interval binning.
5 means optimal binning only.
this function will generate four files in current directory, including 'binGraph.pdf', 'varSummary.csv', 'summaryIV.csv' and 'insignificantVars.csv'(if it exists), as well as mass csv files in '~/binDetails/' subdirectory. If the subdirectory does not exist, it will be created automatically.
A list
Other dataset binning and woe-encoding functions: convertCutPoints
,
dfBinningFun
,
executeBinFun_df
,
genConfigList
, smbinning2
,
woeEncodeFun_df
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