woe_binning: Weight of Evidence based segmentation of a variable
In woeR: Weight of Evidence Based Segmentation of a Variable

Description Usage Arguments Details Value Examples

View source: R/woeR.R

Create heterogeneous segmentations of a numeric variable based on a dependent variable using Weight of Evidence approach

1 2	woe_binning(df, variable, dv, min_perc = 0.02, initial_bins = 50, woe_cutoff = 0.1)

`df`	A data frame containing input arguments - variable & dv
`variable`	character string specifying the column name of the variable you want to bin. Currently, the code supports only numeric and integer classes
`dv`	character string specifying the column name of the binary dependent variable(0,1) (NAs are ignored). Dependent variable should be either of integer or numeric class
`min_perc`	Minimum percentage of records in each segment. If the percentage of records in a segment falls below this threshold it is merged with other segments. Acceptable values are in the range 0.01-0.2
`initial_bins`	No of segments of the variable to be created in the 1st iteration. Default value = 50(2 percent) for sample size > 1500. Acceptable values are in the range 5-100
`woe_cutoff`	Thereshold of the absolute difference in woe values between consecutive segments. If the difference is less than this threshold segments are merged. Acceptable values are in the range 0-0.2

Weight of Evidence represents the natural log of the ratio of percent of 0's in the segment to percent of 1's in the segment. It is a proxy for how far the dv rate for a segment is from the sample dv rate (# of 1s/# of observations).

Output is a list containing the following elements :
a) variable - value of the input argument 'variable'
b) dv - value of the input argument 'dv'
c) breaks - vector specifying cut-off values for each segment. Pass it to 'breaks' argument of cut function to create segments of the variable
d) woe - woe table for the final iteration
e) IV - Information Value for the final iteration