A general method for visualizing and explaining black-box regression models

Authors:
Erik Štrumbelj;Igor Kononenko
Affiliations:
Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia;Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
Venue:
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Year:
2011

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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.