REC curves for visual evaluation of sugarcane yield machine learning models

Abstract

Model validation often is performed with metrics unsuitable for the task. Also, no metric should be used alone as criterion. One alternative is the use of Regression Error Characteristic Curve (REC). The use of REC curves was able to replace the results of single metric evaluation while providing information about trade-offs and model variability. Considering the limitations of plotting several curves, REC curves should be used for final steps of model validation.