Ranking-based evaluation of regression models |
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Authors: | Saharon Rosset Claudia Perlich Bianca Zadrozny |
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Affiliation: | (1) IBM, T. J. Watson Research Center, P.O. Box 218, Yorktown Heights, NY 10598, USA;(2) Computer Science Institute, Federal Fluminense University, Brazil, Rua Passo da Pátria, 156, Bloco E, Sala 302, Niterói, RJ, Brazil, CEP 24210-240 |
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Abstract: | We suggest the use of ranking-based evaluation measures for regression models, as a complement to the commonly used residual-based
evaluation. We argue that in some cases, such as the case study we present, ranking can be the main underlying goal in building
a regression model, and ranking performance is the correct evaluation metric. However, even when ranking is not the contextually
correct performance metric, the measures we explore still have significant advantages: They are robust against extreme outliers
in the evaluation set; and they are interpretable. The two measures we consider correspond closely to non-parametric correlation
coefficients commonly used in data analysis (Spearman's ρ and Kendall's τ); and they both have interesting graphical representations,
which, similarly to ROC curves, offer useful various model performance views, in addition to a one-number summary in the area
under the curve. An interesting extension which we explore is to evaluate models on their performance in “partially” ranking
the data, which we argue can better represent the utility of the model in many cases. We illustrate our methods on a case
study of evaluating IT Wallet size estimation models for IBM's customers.
Saharon Rosset is Research Staff Member in the Data Analytics Research Group at IBM's T. J. Watson Research Center. He received his B.S.
in Mathematics and M.Sc., in Statistics from Tel Aviv University in Israel, and his Ph.D. in Statistics from Stanford University
in 2003. In his research, he aspires to develop practically useful predictive modeling methodologies and tools, and apply
them to solve problems in business and scientific domains. Currently, his major projects include work on customer wallet estimation
and analysis of genetic data.
Claudia Perlich has received a M.Sc. in Computer Science from Colorado University at Boulder, a Diploma in Computer Science from Technische
Universitaet in Darmstadt, and her Ph.D. in Information Systems from Stern School of Business, New York University. Her Ph.D.
thesis concentrated on probability estimation in multi-relational domains that capture information of multiple entity types
and relationships between them. Her dissertation was recognized as an additional winner of the International SAP Doctoral
Support Award Competition. Claudia joined the Data Analytics Research group at IBM's T.J. Watson Research Center as a Research
Staff Member in October 2004. Her research interests are in statistical machine learning for complex real-world domains and
business applications.
Bianca Zadrozny is currently an associate professor at the Computer Science Department of Federal Fluminense University in Brazil. Her research
interests are in the areas of applied machine learning and data mining. She received her B.Sc. in Computer Engineering from
the Pontifical Catholic University in Rio de Janeiro, Brazil, and her M.Sc. and Ph.D. in Computer Science from the University
of California at San Diego. She has also worked as a research staff member in the data analytics research group at IBM T.J.
Watson Research Center. |
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Keywords: | Model evaluation Evaluation robustness Regression Ranking correlation Performance visualization |
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