How to Explain Individual Classification Decisions
Abstract
After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the most likely label of a given unseen data point. However, most methods will provide no answer why the model predicted a particular label for a single instance and what features were most influential for that particular instance. The only method that is currently able to provide such explanations are decision trees. This paper proposes a procedure which (based on a set of assumptions) allows to explain the decisions of any classification method.
Cite
Text
Baehrens et al. "How to Explain Individual Classification Decisions." Journal of Machine Learning Research, 2010.Markdown
[Baehrens et al. "How to Explain Individual Classification Decisions." Journal of Machine Learning Research, 2010.](https://mlanthology.org/jmlr/2010/baehrens2010jmlr-explain/)BibTeX
@article{baehrens2010jmlr-explain,
title = {{How to Explain Individual Classification Decisions}},
author = {Baehrens, David and Schroeter, Timon and Harmeling, Stefan and Kawanabe, Motoaki and Hansen, Katja and Müller, Klaus-Robert},
journal = {Journal of Machine Learning Research},
year = {2010},
pages = {1803-1831},
volume = {11},
url = {https://mlanthology.org/jmlr/2010/baehrens2010jmlr-explain/}
}