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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 3/2014

01.12.2014 | Regular Paper

Explaining prediction models and individual predictions with feature contributions

verfasst von: Erik Štrumbelj, Igor Kononenko

Erschienen in: Knowledge and Information Systems | Ausgabe 3/2014

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Abstract

We present a sensitivity analysis-based method for explaining prediction models that can be applied to any type of classification or regression model. Its advantage over existing general methods is that all subsets of input features are perturbed, so interactions and redundancies between features are taken into account. Furthermore, when explaining an additive model, the method is equivalent to commonly used additive model-specific methods. We illustrate the method’s usefulness with examples from artificial and real-world data sets and an empirical analysis of running times. Results from a controlled experiment with 122 participants suggest that the method’s explanations improved the participants’ understanding of the model.
Vorheriger Artikel Tackling representation, annotation and classification challenges for temporal knowledge base population
Nächster Artikel Efficient mining of discriminative co-clusters from gene expression data

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Metadaten
Titel
Explaining prediction models and individual predictions with feature contributions
verfasst von
Erik Štrumbelj
Igor Kononenko
Publikationsdatum
01.12.2014
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 3/2014
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-013-0679-x

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