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.

Deze oefening maakt deel uit van de cursus

HR Analytics: Predicting Employee Churn in R

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Oefeninstructies

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.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Load Information package
___

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

# Print Information Value 
___

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