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Ranking-based evaluation of regression models | IEEE Conference Publication | IEEE Xplore

Ranking-based evaluation of regression models


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...Show More

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 p and Kendall's r); and they both have interesting graphical representations, which, similarly to ROC curves, offer useful "partial" model performance views, in addition to a one-number summary in the area under the curve. We illustrate our methods on a case study of evaluating IT wallet size estimation models for IBM's customers.
Date of Conference: 27-30 November 2005
Date Added to IEEE Xplore: 03 January 2006
Print ISBN:0-7695-2278-5

ISSN Information:

Conference Location: Houston, TX, USA

References

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