Type: Package
Title: Collapses Levels, Computes Information Value and WoE
Version: 0.3.0
Author: Krishanu Mukherjee
Maintainer: Krishanu Mukherjee <toton1181@gmail.com>
Description: Contains functions to help in selecting and exploring features ( or variables ) in binary classification problems. Provides functions to compute and display information value and weight of evidence (WoE) of the variables , and to convert numeric variables to categorical variables by binning. Functions are also provided to determine which levels ( or categories ) of a categorical variable can be collapsed (or combined ) based on their response rates. The functions provided only work for binary classification problems.
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Imports: dplyr,lazyeval, ggplot2
Depends: magrittr
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-06-04 13:09:20 UTC; User
Repository: CRAN
Date/Publication: 2020-06-04 13:20:02 UTC

German Credit data set

Description

This data set classifies customers as "Good" or "Bad" as per their credit risks.This data set was contributed by Professor Dr. Hans Hofmann,and can be downloaded from the UCI Machine Learning Repository.

Usage

data("German_Credit")

Format

A data frame with 1000 observations on the following 21 variables.

Account_Balance

a factor with levels A11 A12 A13 A14

Duration

a numeric vector

Credit_History

a factor with levels A30 A31 A32 A33 A34

Purpose

a factor with levels A40 A41 A410 A42 A43 A44 A45 A46 A48 A49

Credit_Amount

a numeric vector

Saving_Accounts_Bonds

a factor with levels A61 A62 A63 A64 A65

Current_Employment_Length

a factor with levels A71 A72 A73 A74 A75

Installment_Rate

a numeric vector

MaritalStatusnGender

a factor with levels A91 A92 A93 A94

Guarantors

a factor with levels A101 A102 A103

‘⁠Duration in Current Address⁠’

a numeric vector

Valuable_Asset

a factor with levels A121 A122 A123 A124

Age

a numeric vector

Other_Credit

a factor with levels A141 A142 A143

Housing

a factor with levels A151 A152 A153

Existing_Credits

a numeric vector

Job

a factor with levels A171 A172 A173 A174

Dependents

a numeric vector

Telephone

a factor with levels A191 A192

ForeignWorker

a factor with levels A201 A202

Good_Bad

a numeric vector

Source

https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)

Examples

data(German_Credit)
str(German_Credit)

IVCalc

Description

This function displays the Information Values by the levels of an attribute This information is displayed for all attributes in the data set

Usage

IVCalc(dset, resp = "y", bins = 10, adjFactor = 0.5)

Arguments

dset

The data frame containing the data set

resp

A character respresenting the name of the binary outcome variable The binary outcome variable may be a factor with two levels or an integer (or numeric ) with two unique values

bins

A number denoting the number of bins.Default value is 10

adjFactor

A number or a decimal denoting what is to be added to the number of responses (binary outcome variable is 1 ) or to the number of non responses (binary outcome variable is 0) if either is zero for any level of the attribute

Value

A list containing the tables of Information Values by levels for every attribute

Examples


# Load the German_Credit data set supplied with this package

data("German_Credit")

l<-list()

# Call the function as follows

l<-IVCalc(German_Credit,resp="Good_Bad",bins=10)

# Information Value for  the attribute Account_Balance in the German_Credit data

l$Account_Balance

IVCalc2

Description

This function displays the Information Values of all the attributes in the data set

Usage

IVCalc2(dset, resp = "y", bins = 10, adjFactor = 0.5)

Arguments

dset

The data frame containing the data set

resp

A character respresenting the name of the binary outcome variable The binary outcome variable may be a factor with two levels or an integer (or numeric ) with two unique values

bins

A number denoting the number of bins.Default value is 10

adjFactor

A number or a decimal denoting what is to be added to the number of responses (binary outcome variable is 1 ) or to the number of non responses (binary outcome variable is 0) if either is zero for any level of the attribute

Value

A data frame containing the Information Values for every attribute

Examples


# Load the German_Credit data set supplied with this package

data("German_Credit")

d<-data.frame()

# Call the function as follows

d<-IVCalc2(German_Credit,resp="Good_Bad",bins=10)

# Information Value for all the attributes in the German_Credit data

d

displayIV

Description

This function displays the Information Values of the levels of an attribute.

Usage

displayIV(dset, col = "xyz", resp = "y", adjFactor = 0.5, bins = 10)

Arguments

dset

The data frame containing the data set

col

A character respresenting the name of the attribute . The attribute can either be numeric or categorical

resp

A character respresenting the name of the binary outcome variable The binary outcome variable may be a factor with two levels or an integer (or numeric ) with two unique values

adjFactor

A number or a decimal denoting what is to be added to the number of responses (binary outcome variable is 1 ) or to the number of non responses (binary outcome variable is 0) if either is zero for any level of the attribute

bins

A number denoting the number of bins.Default value is 10

Examples


# Load the German_Credit data set supplied with this package


data("German_Credit")

displayIV(German_Credit,col="Credit_History",resp="Good_Bad")

displayResponseRatebyLevels

Description

This function displays the response percents of the levels of an attribute.

Usage

displayResponseRatebyLevels(
  dset,
  col = "job",
  resp = "Good_Bad",
  bins = 10,
  adjFactor = 0.5
)

Arguments

dset

The data frame containing the data set

col

A character respresenting the name of the attribute . The attribute can either be numeric or categorical

resp

A character respresenting the name of the binary outcome variable The binary outcome variable may be a factor with two levels or an integer (or numeric ) with two unique values

bins

A number denoting the number of bins.Default value is 10

adjFactor

A number or a decimal denoting what is to be added to the number of responses (binary outcome variable is 1 ) or to the number of non responses (binary outcome variable is 0) if either is zero for any level of the attribute

Examples


# Load the German_Credit data set supplied with this package

data("German_Credit")

displayResponseRatebyLevels(German_Credit,col="Credit_History",resp="Good_Bad")

displayWOE

Description

This function displays the Weight of Evidence of the levels of an attribute.

Usage

displayWOE(dset, col = "xyz", resp = "y", adjFactor = 0.5, bins = 10)

Arguments

dset

The data frame containing the data set

col

A character respresenting the name of the attribute . The attribute can either be numeric or categorical

resp

A character respresenting the name of the binary outcome variable The binary outcome variable may be a factor with two levels or an integer (or numeric ) with two unique values

adjFactor

A number or a decimal denoting what is to be added to the number of responses (binary outcome variable is 1 ) or to the number of non responses (binary outcome variable is 0) if either is zero for any level of the attribute

bins

A number denoting the number of bins.Default value is 10

Examples


# Load the German_Credit data set supplied with this package

data("German_Credit")

displayWOE(German_Credit,col="Credit_History",resp="Good_Bad")

levelsCollapser

Description

This function displays the response rates by the levels of an attribute Levels with similar response rates may be combined

Usage

levelsCollapser(dset, resp = "y", bins = 10)

Arguments

dset

The data frame containing the data set

resp

A character respresenting the name of the binary outcome variable The binary outcome variable may be a factor with two levels or an integer (or numeric ) with two unique values

bins

A number denoting the number of bins.Default value is 10

Value

A list containing the tables of response rate by levels for every attribute

Examples


# Load the German_Credit data set supplied with this package

data("German_Credit")

# Create an empty list

l<-list()

# Call the function as follows

l<-levelsCollapser(German_Credit,resp="Good_Bad",bins=10)

# response rate by levels of the Account_Balance in the German_Credit data

l$Account_Balance

# Collapse levels with similar response percentages.

numericToCategorical

Description

This function categorizes a numerical variable by binning

Usage

numericToCategorical(dset, col = "job", resp = "y", bins = 10, adjFactor = 0.5)

Arguments

dset

The data frame containing the data set

col

A character respresenting the name of the numeric attribute which we want to categorize

resp

A character respresenting the name of the binary outcome variable The binary outcome variable may be a factor with two levels or an integer (or numeric ) with two unique values

bins

A number denoting the number of bins.Default value is 10

adjFactor

A number or a decimal denoting what is to be added to the number of responses (binary outcome variable is 1 ) or to the number of non responses (binary outcome variable is 0) if either is zero for any level of the attribute

Value

A list containing the categorized attribute,a table of Information Values for the levels of the categorized attribute,the Information Value for the entire attribute,a table showing the response rates of the levels of the categorized attribute

Examples


# Load the German_Credit data set supplied with this package

data("German_Credit")

# Create an empty list

l<-list()

# Call the function as follows.
#This will categorize the numeric variable Duration in the German_Credit dataset.

l<-numericToCategorical(German_Credit,col="Duration",resp="Good_Bad")


# To view the categorized variable

 l$categoricalVariable

 # To view the IV table of the levels of the categorized variable

 l$IVTable

 # To view the total IV value of the  categorized variable

 l$IV

 # To view the response rates of the levels of the categorized variable

 l$collapseLevels