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.

本练习是课程的一部分

HR Analytics: Predicting Employee Churn in R

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练习说明

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.

交互式实操练习

通过完成这段示例代码来试试这个练习。

# Load Information package
___

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

# Print Information Value 
___

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