Dealing with multicollinearity

In the previous exercise, you found that multicollinearity exists in your model by reviewing the VIF values of independent variables. Follow the steps below to remove multicollinearity:

Step 1: Calculate VIF of the model
Step 2: Identify if any variable has VIF greater than or equal to 5
- Step 2a: Remove the variable from the model if it has a VIF greater than or equal to 5
- Step 2b: If there are multiple variables with VIF greater than 5, only remove the variable with the highest VIF
Step 3: Repeat steps 1 and 2 until VIF of all variables is less than 5

This exercise is part of the course

HR Analytics: Predicting Employee Churn in R

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Hands-on interactive exercise

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

# Remove level
model_1 <- glm(turnover ~ . - ___, family = "binomial", 
               data = train_set_multi)

# Check multicollinearity again
___

# Which variable has the highest VIF value?
highest <- ___

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