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Class 12 Pre-work: ROC Curves and AUC

Watch my video on ROC Curves and Area Under the Curve (14 minutes), and be prepared to discuss it in class on Wednesday. (Feel free to play with the visualization shown in the video, or view the video transcript and screenshots.)

Here are some questions to think about:

  • What is the difference between the predict and predict_proba methods in scikit-learn?
    • The former outputs class predictions, and the latter outputs predicted probabilities of class membership.
  • If you have a classification model that outputs predicted probabilities, how could you convert those probabilities to class predictions?
    • Set a threshold, and classify everything above the threshold as a 1 and everything below the threshold as a 0.
  • Why are predicted probabilities (rather than just class predictions) required to generate an ROC curve?
    • Because an ROC curve is measuring the performance of a classifier at all possible thresholds, and thresholds only make sense in the context of predicted probabilities.
  • Could you use an ROC curve for a regression problem? Why or why not?
    • No, because ROC is a plot of TPR vs FPR, and those concepts have no meaning in a regression problem.
  • What's another term for True Positive Rate?
    • Sensitivity or recall.
  • If I wanted to increase specificity, how would I change the classification threshold?
    • Increase it.
  • Is it possible to adjust your classification threshold such that both sensitivity and specificity increase simultaneously? Why or why not?
    • No, because increasing either of those requires moving the threshold in opposite directions.
  • What are the primary benefits of ROC curves over classification accuracy?
    • Doesn't require setting a classification threshold, allows you to visualize the performance of your classifier, works well for unbalanced classes.
  • What should you do if your AUC is 0.2?
    • Reverse your predictions so that your AUC is 0.8.
  • What would the plot of reds and blues look like for a dataset in which each observation was a credit card transaction, and the response variable was whether or not the transaction was fraudulent? (0 = not fraudulent, 1 = fraudulent)
    • Blues would be significantly larger, lots of overlap between blues and reds.
  • What's a real-world scenario in which you would prefer high specificity (rather than high sensitivity) for your classifier?
    • Speed cameras issuing speeding tickets.