Binning by recursive information gain ratio maximization

Source: R/binning.R

The binning_rgr() finding intervals for numerical variable using recursive information gain ratio maximization.

binning_rgr(.data, y, x, min_perc_bins = 0.1, max_n_bins = 5, ordered = TRUE)

Arguments

.data

a data frame.
y

character. name of binary response variable. The variable must character of factor.
x

character. name of continuous characteristic variable. At least 5 different values. and Inf is not allowed.
min_perc_bins

numeric. minimum percetange of rows for each split or segment (controls the sample size), 0.1 (or 10 percent) as default.
max_n_bins

integer. maximum number of bins or segments to split the input variable, 5 bins as default.
ordered

logical. whether to build an ordered factor or not.

Value

an object of "infogain_bins" class. Attributes of "infogain_bins" class is as follows.

  • class : "infogain_bins".

  • type : binning type, "infogain".

  • breaks : numeric. the number of intervals into which x is to be cut.

  • levels : character. levels of binned value.

  • raw : numeric. raw data, x argument value.

  • target : integer. binary response variable.

  • x_var : character. name of x variable.

  • y_var : character. name of y variable.

Details

This function can be usefully used when developing a model that predicts y.

See also

binning, binning_by, plot.infogain_bins.

Examples

# \donttest{
library(dplyr)

# binning by recursive information gain ratio maximization using character
bin <- binning_rgr(heartfailure, "death_event", "creatinine")

# binning by recursive information gain ratio maximization using name
bin <- binning_rgr(heartfailure, death_event, creatinine)
bin
#> binned type: infogain
#> number of bins: 5
#> x
#> [0.5,1.0)       1.0 [1.1,1.2) [1.2,1.7) [1.7,9.4] 
#>        81        50        43        64        61 

# summary optimal_bins class
summary(bin)
#>      levels freq      rate
#> 1 [0.5,1.0)   81 0.2709030
#> 2       1.0   50 0.1672241
#> 3 [1.1,1.2)   43 0.1438127
#> 4 [1.2,1.7)   64 0.2140468
#> 5 [1.7,9.4]   61 0.2040134

# visualize all information for optimal_bins class
plot(bin)

# visualize WoE information for optimal_bins class
plot(bin, type = "cross")

# visualize all information without typographic
plot(bin, type = "cross", typographic = FALSE)

# extract binned results
extract(bin) %>%
  head(20)
#>  [1] [1.7,9.4] [1.1,1.2) [1.2,1.7) [1.7,9.4] [1.7,9.4] [1.7,9.4] [1.2,1.7)
#>  [8] [1.1,1.2) [1.2,1.7) [1.7,9.4] [1.7,9.4] [0.5,1.0) [1.1,1.2) [1.1,1.2)
#> [15] 1.0       [1.2,1.7) [0.5,1.0) [0.5,1.0) 1.0       [1.7,9.4]
#> Levels: [0.5,1.0) < 1.0 < [1.1,1.2) < [1.2,1.7) < [1.7,9.4]
# }