Abstract
We propose a method for explaining regression models and their predictions for individual instances. The method successfully reveals how individual features influence the model and can be used with any type of regression model in a uniform way. We used different types of models and data sets to demonstrate that the method is a useful tool for explaining, comparing, and identifying errors in regression models. © 2011 Springer-Verlag.
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CITATION STYLE
Štrumbelj, E., & Kononenko, I. (2011). A general method for visualizing and explaining black-box regression models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6594 LNCS, pp. 21–30). https://doi.org/10.1007/978-3-642-20267-4_3
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