others_class: Clubbing of classes of categorical variable with low...

In scorecardModelUtils: Credit Scorecard Modelling Utils

Description

The function groups the classes of a categorical variable which have population percentage less than a threshold as "Low_pop_perc". The user can choose whether to club the missing class or keep it as separate class. The default setting is that missing classes are not treated separately.

Usage

others_class(base, target, column_name, threshold, char_missing = NA)

Arguments

base	input dataframe
target	column / field name for the target variable to be passed as string (must be 0/1 type)
column_name	column name or array of column names of the dataframe on which the operation is to be done
threshold	threshold population percentage below which the class is to be classified as others, to be provided as decimal/fraction
char_missing	(optional) imputed missing value for categorical variable if its to be kept separate (default value is NA)

Value

base	a dataframe after converting all low percentage classes into "Low_pop_perc" class
mapping_table	a dataframe with mapping between original classes which are now "Low_pop_perc" class (if any)

Author(s)

Arya Poddar <[e-mail]>

Examples

data <- iris[c(1:110),]
data$Y <- sample(0:1,size=nrow(data),replace=TRUE)
data$Species <- as.character(data$Species)
data_otherclass <- others_class(base = data,target = "Y",column_name = "Species",threshold = 0.15)

scorecardModelUtils documentation built on May 2, 2019, 9:59 a.m.