What is an ROC Curve?

October 14th, 2016 by

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. (more…)

Measures of Predictive Models: Sensitivity and Specificity

June 5th, 2015 by

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.  (more…)