Learning Decision Trees Using the Area Under the ROC Curve
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
An empirical study of maintenance and development estimation accuracy
Journal of Systems and Software
Regression error characteristic surfaces
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
The use of receiver operating characteristic curves in biomedical informatics
Journal of Biomedical Informatics - Special issue: Clinical machine learning
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Selecting features in microarray classification using ROC curves
Pattern Recognition
Ranking-based evaluation of regression models
Knowledge and Information Systems
Maximizing the area under the ROC curve by pairwise feature combination
Pattern Recognition
Maximizing area under ROC curve for biometric scores fusion
Pattern Recognition
An experimental comparison of performance measures for classification
Pattern Recognition Letters
Tuning Data Mining Methods for Cost-Sensitive Regression: A Study in Loan Charge-Off Forecasting
Journal of Management Information Systems
ROC Curves for Continuous Data
ROC Curves for Continuous Data
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
An extension of the Gauss-Newton algorithm for estimation under asymmetric loss
Computational Statistics & Data Analysis
ICST '10 Proceedings of the 2010 Third International Conference on Software Testing, Verification and Validation
The ROC manifold for classification systems
Pattern Recognition
An extended tuning method for cost-sensitive regression and forecasting
Decision Support Systems
Partial AUC maximization in a linear combination of dichotomizers
Pattern Recognition
Adaptive ROC-based ensembles of HMMs applied to anomaly detection
Pattern Recognition
An online AUC formulation for binary classification
Pattern Recognition
The Journal of Machine Learning Research
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Receiver Operating Characteristic (ROC) analysis is one of the most popular tools for the visual assessment and understanding of classifier performance. In this paper we present a new representation of regression models in the so-called regression ROC (RROC) space. The basic idea is to represent over-estimation against under-estimation. The curves are just drawn by adjusting a shift, a constant that is added (or subtracted) to the predictions, and plays a similar role as a threshold in classification. From here, we develop the notions of optimal operating condition, convexity, dominance, and explore several evaluation metrics that can be shown graphically, such as the area over the RROC curve (AOC). In particular, we show a novel and significant result: the AOC is equivalent to the error variance. We illustrate the application of RROC curves to resource estimation, namely the estimation of software project effort.