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Exercise

Multiple feature engineering steps

The power of the recipes package is that you can include multiple preprocessing steps in a single recipe object. These steps will be carried out in the order they are entered with the step_*() functions.

In this exercise, you will build upon your feature engineering from the last exercise. In addition to removing correlated predictors, you will create a recipe object that also normalizes all numeric predictors in the telecommunications data.

The telecom_training and telecom_test datasets have been loaded into your session.

Instructions 1/3

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  • Create a recipe object, telecom_norm_rec, that sets the outcome variable to canceled_service and all remaining columns in telecom_training to predictor variables.
  • Specify your recipe to first remove correlated predictors at the 0.8 threshold and then normalize all numeric predictor variables.