WOE Binning Adjustment
woebin_adj interactively adjust the binning breaks.
woebin_adj(dt, y, bins, adj_all_var = TRUE, special_values = NULL, method = "tree", save_breaks_list = NULL, count_distr_limit = 0.05, to = "breaks_list", ...)
dt |
A data frame. |
y |
Name of y variable. |
bins |
A list of data frames. Binning information generated from |
adj_all_var |
Logical, whether to show variables have monotonic woe trends. Defaults to TRUE |
special_values |
The values specified in special_values will in separate bins. Defaults to NULL. |
method |
Optimal binning method, it should be "tree" or "chimerge". Defaults to "tree". |
save_breaks_list |
A string. The file name to save breaks_list. Defaults to None. |
count_distr_limit |
The minimum count distribution percentage. Accepted range: 0.01-0.2; Defaults to 0.05. This argument should be the same with woebin's. |
to |
Adjusting bins into breaks_list or bins_list. Defaults to breaks_list. |
... |
Additional parameters. |
A list of modified break points of each x variables.
## Not run:
# Load German credit data
data(germancredit)
# Example I
dt = germancredit[, c("creditability", "age.in.years", "credit.amount")]
bins = woebin(dt, y="creditability")
breaks_adj = woebin_adj(dt, y="creditability", bins)
bins_final = woebin(dt, y="creditability",
breaks_list=breaks_adj)
# Example II
binsII = woebin(germancredit, y="creditability")
breaks_adjII = woebin_adj(germancredit, "creditability", binsII)
bins_finalII = woebin(germancredit, y="creditability",
breaks_list=breaks_adjII)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.