Calculating Information Value

So far you have combined data from multiple sources and created new variables to derive insights from data. Do you think all these variables can explain turnover?

Information Value (IV) helps in measuring and ranking the variables on the basis of the predictive power of each variable. You can use Information Value (IV) to drop the variables which have very low predictive power.

This is a part of the course

“HR Analytics: Predicting Employee Churn in R”

View Course

Exercise instructions

Load the Information package.
Use the emp_final dataset from the previous exercise to find information value of all the variables in the dataset.
Print Information Value (IV) of each variable.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Load Information package
___

# Compute Information Value 
IV <- create_infotables(data = ___, y = ___)

# Print Information Value 
___

Edit and Run Code