Interpreting machine learning models using model-agnostic approach
Author:
Publisher
AIP Publishing
Link
http://aip.scitation.org/doi/pdf/10.1063/5.0143186
Reference13 articles.
1. Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. Knowledge Discovery and Data Mining (KDD).
2. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
3. Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal. 2018. “Explaining Explanations: An Overview of Interpretability of Machine Learning”. The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018).
4. Zachary C. Lipton. 2016. “The Mythos of Model Interpretability”. 2016 ICML Workshop on Human Interpretability in Machine Learning.
5. Finale Doshi-Velez and Been Kim. 2017. “Towards A Rigorous Science of Interpretable Machine Learning”. arXiv:1702.08608 [cs, stat]. ArXiv: 1702.08608.
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