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Exercise

Combining test dataset results

Evaluating your model's performance on the test dataset gives insights into how well your model predicts on new data sources. These insights will help you communicate your model's value in solving problems or improving decision making.

Before you can calculate classification metrics such as sensitivity or specificity, you must create a results tibble with the required columns for yardstick metric functions.

In this exercise, you will use your trained model to predict the outcome variable in the telecom_test dataset and combine it with the true outcome values in the canceled_service column.

Your trained model, logistic_fit, and test dataset, telecom_test, have been loaded from the previous exercise.

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  • Use your trained model and the predict() function to create a tibble, class_preds, with predicted outcome variable categories using the test dataset.