## Regression Error Characteristic Curves

### Jinbo Bi and Kristin Bennett

Department of Mathematical Sciences

Rensselaer Polytechnic Institute

**Abstract.** 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.

- 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 (bennek@rpi.edu) or Jinbo Bi (bij2@rpi.edu) for on-going progress.

Contact Jinbo Bi (jinbo@engr.uconn.edu) for information about this page.