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Exercise

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.

Instructions 1/3
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  • Select all of the numeric columns in the telecom_training data.
  • Create a correlation matrix of the numeric columns of telecom_training.
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