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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: An update. SIGKDD Explorations 11(1), 1–3 (2009)
I-Cheng, Y.: Modeling of strength of high performance concrete using artificial neural networks. Cement and Concrete Research 28(12), 1797–1808 (1998)
Jakulin, A., Možina, M., Demšar, J., Bratko, I., Zupan, B.: Nomograms for visualizing support vector machines. In: KDD 2005: ACM SIGKDD, pp. 108–117 (2005)
Lemaire, V., Fraud, R., Voisine, N.: Contact personalization using a score understanding method. In: IJCNN 2008 (2008)
Možina, M., Demšar, J., Kattan, M., Zupan, B.: Nomograms for visualization of naive bayesian classifier. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 337–348. Springer, Heidelberg (2004)
Poulet, F.: Svm and graphical algorithms: A cooperative approach. In: 4th IEEE ICDM, pp. 499–502 (2004)
Robnik-Šikonja, M., Kononenko, I.: Explaining classifications for individual instances. IEEE TKDE 20, 589–600 (2008)
Štrumbelj, E., Kononenko, I.: An efficient explanation of individual classifications using game theory. Journal of Machine Learning Research 11, 1–18 (2010)
Zien, A., Krämer, N., Sonnenburg, S., Rätsch, G.: The feature importance ranking measure. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5782, pp. 694–709. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Štrumbelj, E., Kononenko, I. (2011). A General Method for Visualizing and Explaining Black-Box Regression Models. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20267-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-20267-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20266-7
Online ISBN: 978-3-642-20267-4
eBook Packages: Computer ScienceComputer Science (R0)