Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

@article{Letham2015InterpretableCU,
  title={Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model},
  author={Benjamin Letham and C. Rudin and T. McCormick and D. Madigan},
  journal={ArXiv},
  year={2015},
  volume={abs/1511.01644}
}
We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. [...] Key Method It employs a novel prior structure to encourage sparsity. Our experiments show that Bayesian Rule Lists has predictive accuracy on par with the current top algorithms for prediction in machine learning. Our method is motivated by recent developments in personalized medicine, and can be used to produce highly accurate and interpretable medical scoring systems. We demonstrate this by…Expand
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