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Exercise

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.

Instructions 1/3

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  • Create a tibble, threshold_df, which contains the sensitivity and specificity of your classification model across the unique probability thresholds in telecom_results.
  • Print threshold_df to view the results.