Discovering correlated predictors
Correlated predictor variables provide redundant information and can negatively impact the model fitting process. When two variables are highly correlated, their values change linearly with each other and hence provide the same information to your machine learning algorithms. This phenomenon is know as multicollinearity.
Before beginning the model fitting process, it's important to explore your dataset to uncover these relationships and remove them in your feature engineering steps.
In this exercise, you will explore the telecom_training dataset by creating a correlation matrix of all the numeric predictor variables.
The telecom_training data has been loaded into your session.
Este exercício faz parte do curso
Modeling with tidymodels in R
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
telecom_training %>%
# Select numeric columns
___(___) %>%
# Calculate correlation matrix
___