one_hot: One Hot Encoding

In scorecard: Credit Risk Scorecard

One Hot Encoding

Description

One-hot encoding on categorical variables and replace missing values. It is not needed when creating a standard scorecard model, but required in models that without doing woe transformation.

Usage

one_hot(dt, var_skip = NULL, var_encode = NULL, nacol_rm = FALSE, ...)

Arguments

dt	A data frame.
var_skip	Name of categorical variables that will skip for one-hot encoding. Defaults to NULL.
var_encode	Name of categorical variables to be one-hot encoded, Defaults to NULL. If it is NULL, then all categorical variables except in var_skip are counted.
nacol_rm	Logical. One-hot encoding on categorical variable contains missing values, whether to remove the column generated to indicate the presence of NAs. Defaults to FALSE.
...	Additional parameters.

Value

A data frame

Examples

# load germancredit data
data(germancredit)

library(data.table)
dat = rbind(
  setDT(germancredit)[, c(sample(20,3),21)],
  data.table(creditability=sample(c("good","bad"),10,replace=TRUE)),
  fill=TRUE)

# one hot encoding
## keep na columns from categorical variable
dat_onehot1 = one_hot(dat, var_skip = 'creditability', nacol_rm = FALSE) # default
str(dat_onehot1)
## remove na columns from categorical variable
dat_onehot2 = one_hot(dat, var_skip = 'creditability', nacol_rm = TRUE)
str(dat_onehot2)

scorecard documentation built on Aug. 8, 2023, 5:07 p.m.