woebin_ply converts original values of input data into woe or bin based on the binning information generated from woebin.

woebin_ply(dt, bins, to = "woe", no_cores = NULL, print_step = 0L,
  replace_blank_inf = TRUE, ...)

Arguments

dt

A data frame.

bins

Binning information generated from woebin.

to

Converting original values to woe or bin. Defaults to woe.

no_cores

Number of CPU cores for parallel computation. Defaults to 90 percent of total cpu cores.

print_step

A non-negative integer. Defaults to 1. If print_step>0, print variable names by each print_step-th iteration. If print_step=0 or no_cores>1, no message is print.

replace_blank_inf

Logical. Replace blank values with NA and infinite with -1. Defaults to TRUE. This argument should be the same with woebin's.

...

Additional parameters.

Value

A data frame with columns for variables converted into woe values.

See also

Examples

# load germancredit data data(germancredit) # Example I dt = germancredit[, c("creditability", "credit.amount", "purpose")] # binning for dt bins = woebin(dt, y = "creditability")
#> [INFO] creating woe binning ...
# converting to woe dt_woe = woebin_ply(dt, bins=bins)
#> [INFO] converting into woe values ...
str(dt_woe)
#> Classes ‘data.table’ and 'data.frame': 1000 obs. of 3 variables: #> $ creditability : Factor w/ 2 levels "bad","good": 2 1 2 2 1 2 2 2 2 1 ... #> $ credit.amount_woe: num 0.0337 0.3905 -0.2583 0.3905 0.3905 ... #> $ purpose_woe : num -0.41 -0.41 0.28 0.28 0.28 ... #> - attr(*, ".internal.selfref")=<externalptr>
# converting to bin dt_bin = woebin_ply(dt, bins=bins, to = 'bin')
#> [INFO] converting into woe values ...
str(dt_bin)
#> Classes ‘data.table’ and 'data.frame': 1000 obs. of 3 variables: #> $ creditability : Factor w/ 2 levels "bad","good": 2 1 2 2 1 2 2 2 2 1 ... #> $ credit.amount_bin: chr "[-Inf,1400)" "[4000,9200)" "[1800,4000)" "[4000,9200)" ... #> $ purpose_bin : chr "radio/television" "radio/television" "furniture/equipment%,%domestic appliances%,%business%,%repairs%,%car (new)%,%others%,%education" "furniture/equipment%,%domestic appliances%,%business%,%repairs%,%car (new)%,%others%,%education" ... #> - attr(*, ".internal.selfref")=<externalptr>
# \donttest{ # Example II # binning for germancredit dataset bins_germancredit = woebin(germancredit, y="creditability")
#> [INFO] creating woe binning ...
# converting the values in germancredit to woe # bins is a list which generated from woebin() germancredit_woe = woebin_ply(germancredit, bins_germancredit)
#> [INFO] converting into woe values ...
# bins is a data frame bins_df = data.table::rbindlist(bins_germancredit) germancredit_woe = woebin_ply(germancredit, bins_df)
#> [INFO] converting into woe values ...
# }