| Title: | Collapses Levels, Computes Information Value and WoE |
|---|---|
| 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. |
| Authors: | Krishanu Mukherjee |
| Maintainer: | Krishanu Mukherjee <[email protected]> |
| License: | GPL-2 |
| Version: | 0.3.0 |
| Built: | 2025-02-11 03:41:39 UTC |
| Source: | https://github.com/cran/CollapseLevels |
This function displays the Information Values of the levels of an attribute.
displayIV(dset, col = "xyz", resp = "y", adjFactor = 0.5, bins = 10)displayIV(dset, col = "xyz", resp = "y", adjFactor = 0.5, bins = 10)
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 |
# Load the German_Credit data set supplied with this package data("German_Credit") displayIV(German_Credit,col="Credit_History",resp="Good_Bad")# Load the German_Credit data set supplied with this package data("German_Credit") displayIV(German_Credit,col="Credit_History",resp="Good_Bad")
This function displays the response percents of the levels of an attribute.
displayResponseRatebyLevels( dset, col = "job", resp = "Good_Bad", bins = 10, adjFactor = 0.5 )displayResponseRatebyLevels( dset, col = "job", resp = "Good_Bad", bins = 10, adjFactor = 0.5 )
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 |
# Load the German_Credit data set supplied with this package data("German_Credit") displayResponseRatebyLevels(German_Credit,col="Credit_History",resp="Good_Bad")# Load the German_Credit data set supplied with this package data("German_Credit") displayResponseRatebyLevels(German_Credit,col="Credit_History",resp="Good_Bad")
This function displays the Weight of Evidence of the levels of an attribute.
displayWOE(dset, col = "xyz", resp = "y", adjFactor = 0.5, bins = 10)displayWOE(dset, col = "xyz", resp = "y", adjFactor = 0.5, bins = 10)
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 |
# Load the German_Credit data set supplied with this package data("German_Credit") displayWOE(German_Credit,col="Credit_History",resp="Good_Bad")# Load the German_Credit data set supplied with this package data("German_Credit") displayWOE(German_Credit,col="Credit_History",resp="Good_Bad")
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.
data("German_Credit")data("German_Credit")
A data frame with 1000 observations on the following 21 variables.
Account_Balancea factor with levels A11 A12 A13 A14
Durationa numeric vector
Credit_Historya factor with levels A30 A31 A32 A33 A34
Purposea factor with levels A40 A41 A410 A42 A43 A44 A45 A46 A48 A49
Credit_Amounta numeric vector
Saving_Accounts_Bondsa factor with levels A61 A62 A63 A64 A65
Current_Employment_Lengtha factor with levels A71 A72 A73 A74 A75
Installment_Ratea numeric vector
MaritalStatusnGendera factor with levels A91 A92 A93 A94
Guarantorsa factor with levels A101 A102 A103
a numeric vector
Valuable_Asseta factor with levels A121 A122 A123 A124
Agea numeric vector
Other_Credita factor with levels A141 A142 A143
Housinga factor with levels A151 A152 A153
Existing_Creditsa numeric vector
Joba factor with levels A171 A172 A173 A174
Dependentsa numeric vector
Telephonea factor with levels A191 A192
ForeignWorkera factor with levels A201 A202
Good_Bada numeric vector
https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)
data(German_Credit) str(German_Credit)data(German_Credit) str(German_Credit)
This function displays the Information Values by the levels of an attribute This information is displayed for all attributes in the data set
IVCalc(dset, resp = "y", bins = 10, adjFactor = 0.5)IVCalc(dset, resp = "y", bins = 10, adjFactor = 0.5)
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 |
A list containing the tables of Information Values by levels for every attribute
# 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$Account_Balance# Load the German_Credit data set supplied with this package data("German_Credit") l<-list() # Call the function as follows l<-IVCalc(German_Credit,resp="Good_Bad",bins=10) # Information Value for the attribute Account_Balance in the German_Credit data l$Account_Balance
This function displays the Information Values of all the attributes in the data set
IVCalc2(dset, resp = "y", bins = 10, adjFactor = 0.5)IVCalc2(dset, resp = "y", bins = 10, adjFactor = 0.5)
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 |
A data frame containing the Information Values for every attribute
# 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# 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
This function displays the response rates by the levels of an attribute Levels with similar response rates may be combined
levelsCollapser(dset, resp = "y", bins = 10)levelsCollapser(dset, resp = "y", bins = 10)
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 |
A list containing the tables of response rate by levels for every attribute
# 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$Account_Balance # Collapse levels with similar response percentages.# Load the German_Credit data set supplied with this package data("German_Credit") # Create an empty list l<-list() # Call the function as follows l<-levelsCollapser(German_Credit,resp="Good_Bad",bins=10) # response rate by levels of the Account_Balance in the German_Credit data l$Account_Balance # Collapse levels with similar response percentages.
This function categorizes a numerical variable by binning
numericToCategorical(dset, col = "job", resp = "y", bins = 10, adjFactor = 0.5)numericToCategorical(dset, col = "job", resp = "y", bins = 10, adjFactor = 0.5)
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 |
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
# 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# 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