Regression Error Characteristic Curves
Jinbo Bi and Kristin Bennett
Receiver Operating Characteristic (ROC) curves provide a powerful
tool for visualizing and comparing classification results.
Regression Error Characteristic (REC) curves generalize ROC curves
to regression. REC curves plot the error tolerance on the x-axis
versus the percentage of points predicted within the tolerance on
the y-axis. The resulting curve estimates the cumulative
distribution function of the error. The REC curve visually
presents commonly used statistics. The area-over-the-curve (AOC)
is a biased estimate of the expected error. The R-squared value can
be estimated using the ratio of the AOC for a given model to the
AOC for the null model. Users can quickly assess the relative
merits of many regression functions by examining the relative
position of their REC curves. The shape of the curve reveals
additional information that can be used to guide modelling.
Department of Mathematical Sciences
Rensselaer Polytechnic Institute
- This paper has been accepted by the 20th International Conference on Machine Learning, 2003.
- The Matlab package for plotting REC curves:
This is an open source program for non-commercial use only. It provides a preliminary result on our REC
curve analysis and please contact either Dr. Kristin Bennett (firstname.lastname@example.org) or Jinbo Bi (email@example.com)
for on-going progress.
Contact Jinbo Bi (firstname.lastname@example.org) for information about this page.