Regression Error Characteristic Surfaces

• Type: Article in International Conference Proceedings Book
• Date: 2005

Authors

Torgo, L (Author) FEP

Conference proceedings International

Title: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages: 697-702

KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Chicago, IL, 21 August 2005 through 24 August 2005

Indexing

ISI Web of Knowledge

Scopus - 19 Citations

Other information

Authenticus ID: P-007-F0R

DOI: 10.1145/1081870.1081959

Abstract (EN): This paper presents a generalization of Regression Error Characteristic (REC) curves. REC curves describe the cumulative distribution function of the prediction error of models and can be seen as a generalization of ROC curves to regression problems. REC curves provide useful information for analyzing the performance of models, particularly when compared to error statistics like for instance the Mean Squared Error. In this paper we present Regression Error Characteristic (REC) surfaces that introduce a further degree of detail by plotting the cumulative distribution function of the errors across the distribution of the target variable, i.e. the joint cumulative distribution function of the errors and the target variable. This provides a more detailed analysis of the performance of models when compared to REC curves. This extra detail is particularly relevant in applications with non-uniform error costs, where it is important to study the performance of models for specific ranges of the target variable. In this paper we present the notion of REC surfaces, describe how to use them to compare the performance of models, and illustrate their use with an important practical class of applications: the prediction of rare extreme values. Copyright 2005 ACM.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6