2011 | OriginalPaper | Buchkapitel
A General Method for Visualizing and Explaining Black-Box Regression Models
verfasst von : Erik Štrumbelj, Igor Kononenko
Erschienen in: Adaptive and Natural Computing Algorithms
Verlag: Springer Berlin Heidelberg
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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.