• DOI:10.1145/2939672.2939874
Corpus ID: 12533380

Interpretable Decision Sets: A Joint Framework for Description and Prediction

@article{Lakkaraju2016InterpretableDS,
  title={Interpretable Decision Sets: A Joint Framework for Description and Prediction},
  author={H. Lakkaraju and Stephen H. Bach and J. Leskovec},
  journal={Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  year={2016}
}

H. Lakkaraju, Stephen H. Bach, J. Leskovec

Published 2016

Mathematics, Computer Science, Medicine

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

One of the most important obstacles to deploying predictive models is the fact that humans do not understand and trust them. Knowing which variables are important in a model's prediction and how they are combined can be very powerful in helping people understand and trust automatic decision making systems. Here we propose interpretable decision sets, a framework for building predictive models that are highly accurate, yet also highly interpretable. Decision sets are sets of independent if-then… CONTINUE READING

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