loanData

A dataset containing simulated data about the characteristic of a customer applying for a loan. The dependent variable is "bad_flag" which indicates whether the customer defaults or not.

datasets

Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) <doi:10.1198/106186006X133933>.

Sneha Tody

logiBin

Binning Variables to Use in Logistic Regression

loanData function

A data frame with 100 rows and 6 variables:<dl class="dl-horizontal">
 <dt>bad_flag</dt><dd>Indicates whether a customer has defaulted or not</dd>
 <dt>age</dt><dd>Age of the customer</dd>
 <dt>LTV</dt><dd>Ratio of amount of loan to amount of collateral</dd>
 <dt>location</dt><dd>Indicates the location of the customer</dd>
 <dt>balance</dt><dd>Account balance</dd>
 <dt>score</dt><dd>credit score of the customer</dd>
</dl>

Format

Simulated default data of 100 customers — loanData

A data frame with 100 rows and 6 variables:<dl class='dl-horizontal'>
 <dt>bad_flag</dt><dd>Indicates whether a customer has defaulted or not</dd>
 <dt>age</dt><dd>Age of the customer</dd>
 <dt>LTV</dt><dd>Ratio of amount of loan to amount of collateral</dd>
 <dt>location</dt><dd>Indicates the location of the customer</dd>
 <dt>balance</dt><dd>Account balance</dd>
 <dt>score</dt><dd>credit score of the customer</dd>
</dl>

loanData: Simulated default data of 100 customers

Description

Usage

Arguments

Format