scoring: Scoring a dataset with class based on a scalling logic to...

In scorecardModelUtils: Credit Scorecard Modelling Utils

Description

The function takes the data, with each variable as class. The dataframe of class scalling is used to convert the class into scores and finally arrive at the row level final scores by adding up the score values.

Usage

scoring(base, target, scalling)

Arguments

base	input dataframe with classes same as scalling logic
target	column / field name for the target variable to be passed as string (must be 0/1 type)
scalling	dataframe of class scalling with atleast two columns - Variable, Category, Coefficient, D(i,j)_hat, Score

Value

The function returns a dataframe with classes converted to scores and the final score for each record in the input dataframe.

Author(s)

Arya Poddar <[e-mail]>

Examples

data <- iris
suppressWarnings(RNGversion('3.5.0'))
set.seed(11)
data$Y <- sample(0:1,size=nrow(data),replace=TRUE)
x <- c("Sepal.Length","Sepal.Width","Petal.Length","Petal.Width")
iv_table_list <- iv_table(base = data,target = "Y",num_var_name = x,cat_var_name = "Species")
num_cat <- num_to_cat(base = data,num_woe_table = iv_table_list$num_woe_table)
log_model <- glm(Y ~ ., data = num_cat, family = "binomial")
scaling_tab <- scalling(base = num_cat,target = "Y",model = log_model)
score_tab <- scoring(base = num_cat,target = "Y",scalling = scaling_tab)

Example output

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