Abstract
We propose a simple definition of an explanation for the outcome of a classifier based on concepts from causality. We compare it with previously proposed notions of explanation, and study their complexity. We conduct an experimental evaluation with two real datasets from the financial domain.
Cite
CITATION STYLE
APA
Bertossi, L., Li, J., Schleich, M., Suciu, D., & Vagena, Z. (2020). Causality-based Explanation of Classification Outcomes. In Proceedings of the 4th Workshop on Data Management for End-To-End Machine Learning, DEEM 2020 - In conjunction with the 2020 ACM SIGMOD/PODS Conference. Association for Computing Machinery, Inc. https://doi.org/10.1145/3399579.3399865
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
Already have an account? Sign in
Sign up for free