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.
Questo esercizio fa parte del corso
HR Analytics: Predicting Employee Churn in R
Istruzioni dell'esercizio
- Load the
Informationpackage. - Use the
emp_finaldataset from the previous exercise to find information value of all the variables in the dataset. - Print Information Value (IV) of each variable.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Load Information package
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# Compute Information Value
IV <- create_infotables(data = ___, y = ___)
# Print Information Value
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