Feature engineering process

To incorporate feature engineering into the modeling process, the training and test datasets must be preprocessed before the model fitting stage. With the new skills you have learned in this chapter, you will be able to use all of the available predictor variables in the telecommunications data to train your logistic regression model.

In this exercise, you will create a feature engineering pipeline on the telecommunications data and use it to transform the training and test datasets.

The telecom_training and telecom_test datasets as well as your logistic regression model specification, logistic_model, have been loaded into your session.

Bu egzersiz

Modeling with tidymodels in R

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

telecom_recipe <- recipe(___, data = ___) %>% 
  # Removed correlated predictors
  ___(___) %>% 
  # Log transform numeric predictors
  ___(___, base = 10) %>%
  # Normalize numeric predictors
  ___(___) %>%
  # Create dummy variables
  ___(___, ___)

Kodu Düzenle ve Çalıştır