An Efficient Explanation of Individual Classifications Using Game Theory
Abstract
We present a general method for explaining individual predictions of classification models. The method is based on fundamental concepts from coalitional game theory and predictions are explained with contributions of individual feature values. We overcome the method's initial exponential time complexity with a sampling-based approximation. In the experimental part of the paper we use the developed method on models generated by several well-known machine learning algorithms on both synthetic and real-world data sets. The results demonstrate that the method is efficient and that the explanations are intuitive and useful.
Cite
Text
Štrumbelj and Kononenko. "An Efficient Explanation of Individual Classifications Using Game Theory." Journal of Machine Learning Research, 2010.Markdown
[Štrumbelj and Kononenko. "An Efficient Explanation of Individual Classifications Using Game Theory." Journal of Machine Learning Research, 2010.](https://mlanthology.org/jmlr/2010/strumbelj2010jmlr-efficient/)BibTeX
@article{strumbelj2010jmlr-efficient,
title = {{An Efficient Explanation of Individual Classifications Using Game Theory}},
author = {Štrumbelj, Erik and Kononenko, Igor},
journal = {Journal of Machine Learning Research},
year = {2010},
pages = {1-18},
volume = {11},
url = {https://mlanthology.org/jmlr/2010/strumbelj2010jmlr-efficient/}
}