By: Bart Baesens, Seppe vanden Broucke
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You asked: What is a REC curve in a regression context?
Our answer:
A regression error characteristic or REC curve is a regression variant of the ROC curve in classification and plots 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 error on the X-axis can be defined as the squared error or the absolute deviation . Just as with the ROC curve, the perfect model is situated in the upper left corner. Hence, the quicker the curve approaches this point, the better the model. The area above the curve then represents an overall error measure which should preferably be as small as possible. As an example, consider the data represented below.
| Tolerance (X) | Correct predictions (cumulative) |
Cumulative Accuracy (Y) |
| 0 | 1 | 10% |
| 0,05 | 4 | 40% |
| 0,1 | 7 | 70% |
| 0,2 | 9 | 90% |
| 0,5 | 10 | 100% |
The corresponding REC curve is depicted in the following figure.
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