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ROC Curve

What Is an ROC Curve?

by Karen Grace-Martin  2 Comments

An incredibly useful tool in evaluating and comparing predictive models is the ROC curve.

Its name is indeed strange. ROC stands for Receiver Operating Characteristic. Its origin is from sonar back in the 1940s. ROCs were used to measure how well a sonar signal (e.g., from an enemy submarine) could be detected from noise (a school of fish).

ROC curves are a nice way to see how any predictive model can distinguish between the true positives and negatives. [Read more…] about What Is an ROC Curve?

Tagged With: decision rules, logistic regression, predicted probability, ROC Curve, sensitivity

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Measures of Predictive Models: Sensitivity and Specificity

by Karen Grace-Martin  5 Comments

Not too long ago, I was  in Syracuse for a family trip to the zoo. Syracuse is about 60 miles from where I live and it has a very nice little zoo.

This year was particularly exciting because a Trader Joe’s just opened in Syracuse.  We don’t have one where we live (sadly!)  so we always stock up on our favorite specialty groceries when we’re near a Trader Joe’s.

On this particular trip, though, we had an unwelcome surprise.  My credit card card company believed my Trader Joe’s spree was fraudulent and declined the transaction.  I got a notice on my phone and was able to fix it right away, so it wasn’t the big inconvenience it could have been.

But this led us to wonder what it was about the transaction that led the bank to believe it was fraudulent.  Do credit card thieves often skip town and go grocery shopping?

The bank was clearly betting so.  It must have a model for aspects of a transaction that are likely enough to be fraudulent that it shuts it down.  [Read more…] about Measures of Predictive Models: Sensitivity and Specificity

Tagged With: logistic regression, model accuracy, ROC Curve, sensitivity, specificity

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Member Training: ROC Curves

by Karen Grace-Martin  Leave a Comment

ROC Curves are incredibly useful in evaluating any model or process that predicts group membership of individuals.

ROC stands for Receiver Operating Characteristic. This strange name goes back to its original use of assessing the accuracy of sonar readings. Any ROC can tell you how well a process or model distinguishes between true and false positives and negatives.

In this webinar, we’ll talk about what ROC Curves do, when they’re useful, and how to interpret the curve and some related statistics.


Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

Not a Member? Join!

About the Instructor

Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.

She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.

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It’s never too early to set yourself up for successful analysis with support and training from expert statisticians. Just head over and sign up for Statistically Speaking.

You'll get access to this training webinar, 100+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.

Tagged With: ROC Curve

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