Corpus ID: 90260109

Interpreting Black Box Models with Statistical Guarantees

@article{Burns2019InterpretingBB,
  title={Interpreting Black Box Models with Statistical Guarantees},
  author={Collin Burns and Jesse Thomason and Wesley Tansey},
  journal={ArXiv},
  year={2019},
  volume={abs/1904.00045}
}
While many methods for interpreting machine learning models have been proposed, they are frequently ad hoc, difficult to evaluate, and come with no statistical guarantees on the error rate. This is especially problematic in scientific domains, where interpretations must be accurate and reliable. In this paper, we cast black box model interpretation as a hypothesis testing problem. The task is to discover "important" features by testing whether the model prediction is significantly different… Expand
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