Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance
Abstract
At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model's behavior. Assumed in this question are three properties of the interpretable output: coverage, precision, and effort. Coverage refers to how often humans think they can predict the model's behavior, precision to how accurate humans are in those predictions, and effort is either the up-front effort required in interpreting the model, or the effort required to make predictions about a model's behavior. In this work, we propose anchor-LIME (aLIME), a model-agnostic technique that produces high-precision rule-based explanations for which the coverage boundaries are very clear. We compare aLIME to linear LIME with simulated experiments, and demonstrate the flexibility of aLIME with qualitative examples from a variety of domains and tasks.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2016
- DOI:
- 10.48550/arXiv.1611.05817
- arXiv:
- arXiv:1611.05817
- Bibcode:
- 2016arXiv161105817T
- Keywords:
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- Statistics - Machine Learning;
- Computer Science - Artificial Intelligence;
- Computer Science - Machine Learning
- E-Print:
- Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems