ROC curves and area under the ROC curve
ROC curves are used to visualize the performance of a classification model across a range of probability thresholds. An ROC curve with the majority of points near the upper left corner of the plot indicates that a classification model is able to correctly predict both the positive and negative outcomes correctly across a wide range of probability thresholds.
The area under this curve provides a letter grade summary of model performance.
In this exercise, you will create an ROC curve from your logistic regression model results and calculate the area under the ROC curve with yardstick.
Your model results tibble, telecom_results has been loaded into your session.
This exercise is part of the course
Modeling with tidymodels in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate metrics across thresholds
threshold_df <- ___ %>%
___(truth = ___, ___)
# View results
threshold_df