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

Bu egzersiz

HR Analytics: Predicting Employee Churn in R

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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

# Check multicollinearity again
___

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

Kodu Düzenle ve Çalıştır