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Weight of evidence and information value for categorical data.

Usage

rbin_factor(data = NULL, response = NULL, predictor = NULL, include_na = TRUE)

# S3 method for class 'rbin_factor'
plot(x, print_plot = TRUE, ...)

Arguments

data

A data.frame or tibble.

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)