Evaluating performance with yardstick

In the previous exercise, you calculated classification metrics from a sample confusion matrix. The yardstick package was designed to automate this process.

For classification models, yardstick functions require a tibble of model results as the first argument. This should include the actual outcome values, predicted outcome values, and estimated probabilities for each value of the outcome variable.

In this exercise, you will use the results from your logistic regression model, telecom_results, to calculate performance metrics.

The telecom_results tibble has been loaded into your session.