Information value

HR Analytics: Predicting Employee Churn in R

Abhishek Trehan

People Analytics Practitioner

Understanding Information value

Measure of the predictive power of independent variable to accurately predict the dependent variable
Rank independent variables on the basis of their predictive power

Calculating Information value

$$ IV = (\sum \text{(\% of non-events - \% of events)}) * \log(\frac{\text{\% of non-events}}{\text{\% of events}}) $$

Calculating Information value

# Load Information package
library(Information)
# Compute Information Value 
IV <- create_infotables(data = emp_final, y = "turnover")
# Print Information Value 
IV$Summary

                       Variable           IV
12                 percent_hike 1.144784e+00
17             total_dependents 1.088645e+00
21              no_leaves_taken 9.404533e-01
31                       tenure 9.332570e-01
27            mgr_effectiveness 6.830020e-01
11                 compensation 6.074885e-01

Information value (IV) table

Information value	Predictive power
< 0.15	Poor
Between 0.15 and 0.4	Moderate
> 0.4	Strong

percent_hike: 1.14 (Strong)
compa_ratio: 0.29 (Moderate)

Let's practice!

HR Analytics: Predicting Employee Churn in R