I am finishing up a work-note that has some really neat implications as to why working with AUC is more powerful than one might think. I think I am far enough along to share the consequences here. This started as some, now reappraised, thoughts on the fallacy of thinking knowing […]
0.83 (or more precisely 5/6) is a special Area Under the Curve (AUC), which we will show in this note.
Recently Microsoft Data Scientist Bob Horton wrote a very nice article on ROC plots. We expand on this a bit and discuss some of the issues in computing “area under the curve” (AUC).
At Strata+Hadoop World “R Day” Tutorial, Tuesday, March 29 2016, San Jose, California we spent some time on classifier measures derived from the so-called “confusion matrix.” We repeated our usual admonition to not use “accuracy itself” as a project quality goal (business people tend to ask for it as it […]
A bit more on the ROC/AUC The issue The receiver operating characteristic curve (or ROC) is one of the standard methods to evaluate a scoring system. Nina Zumel has described its application, but I would like to call out some additional details. In my opinion while the ROC is a […]