Weight of evidence and information value for categorical data.
Usage
rbin_factor(data = NULL, response = NULL, predictor = NULL, include_na = TRUE)
# S3 method for rbin_factor
plot(x, print_plot = TRUE, ...)Arguments
- data
A
data.frameortibble.- response
Response variable.
- predictor
Predictor variable.
- include_na
logical; if
TRUE, a separate bin is created for missing values.- x
An object of class
rbin_factor.- print_plot
logical; if
TRUE, prints the plot else returns a plot object.- ...
further arguments passed to or from other methods.
Examples
bins <- rbin_factor(mbank, y, education)
bins
#> Binning Summary
#> ---------------------------
#> Method Custom
#> Response y
#> Predictor education
#> Levels 4
#> Count 4521
#> Goods 517
#> Bads 4004
#> Entropy 0.51
#> Information Value 0.05
#>
#>
#> level bin_count good bad woe iv entropy
#> 1 tertiary 1299 195 1104 -0.3133106 0.031785905 0.6101292
#> 2 secondary 2352 231 2121 0.1702190 0.014113157 0.4633093
#> 3 primary 691 66 625 0.2010906 0.005717878 0.4546110
#> 4 unknown 179 25 154 -0.2289295 0.002265111 0.5833603
# plot
plot(bins)
