smbinning2: Auxiliary Function: Optimally Binning of Given Variable
In xxzcool/scoremodel: Credit Scoring Modeling Toolbox

Description Usage Arguments Details Value See Also Examples

Auxiliary function: smbinning2 is an enhanced and integrated optimal binning function for score model, which contains 4 different binning methods(see details) for numeric and factor variables.

1	smbinning2(df, x, binMethod, p, maxcat, aliquots)

`df`	A dataframe only with Xs and Y variables, and the last variable must be Y, see details.
`x`	A name character of one x variable(if x variable is a character variable, it must be converted to factor in advance), `x` must not have a dot, see details.
`binMethod`	An integer from 1 to 4, indicates 4 different binning methods(see details).
`p`	A numeric, means percentage of records per bin, from 0 to 0.5.
`maxcat`	An integer, specifies the maximum number of categories.
`aliquots`	An integer, specifies the number of bins for equal-frequency or equal-interval binning method.

the last variable in df must be binary response variable (0,1). Integer (int) is required. Name of y must not have a dot. Name "default" is not allowed. binMethod=c(1,2,3,4), meanings: 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. when x represents a continuous variable: At least 5 different values(excluding NA). Value Inf is not allowed. when x represents a factor variable: At least 2 different values(excluding NA). Value Inf is not allowed.

A dataframe with cutpoints and corresponding woes

Other dataset binning and woe-encoding functions: convertCutPoints, dfBinningFun, executeBinFun_df, genConfigList, preBinningFun, woeEncodeFun_df

data(CreditData)
mysample <- convertType(CreditData, toType="fac")
splitresult <- splitData(mysample, size = 0.7, ifpercent = TRUE)
train <- splitresult[[1]]
test <- splitresult[[2]]
smbinning2(train, x = "bscore", binMethod = 1, p = 0.05, maxcat = 10, aliquots = 5)