Model performance metrics

In this exercise, you will use yardstick metric functions to evaluate your model's performance on the test dataset.

When you fit a logistic regression model to the telecommunications data in Chapter 2, you predicted canceled_service using avg_call_mins, avg_intl_mins, and monthly_charges. The sensitivity of your model was 0.42 while the specificity was 0.895.

Now that you have incorporated all available predictor variables using feature engineering, you can compare your new model's performance to your previous results.

Your model results, telecom_results, have been loaded into your session.

Deze oefening maakt deel uit van de cursus

Modeling with tidymodels in R

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Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Create a confusion matrix
telecom_results %>% 
  ___(truth = ___, estimate = ___)

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