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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    References

    SHOWING 1-7 OF 7 REFERENCES
    Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
    • 390
    • Highly Influential
    • PDF
    A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems
    • 1,507
    • Highly Influential
    • PDF
    Integrating Classification and Association Rule Mining
    • 2,497
    • Highly Influential
    • PDF
    Optimal marketing strategies over social networks
    • 357
    • Highly Influential
    • PDF
    The CN2 Induction Algorithm
    • 1,132
    • Highly Influential
    • PDF
    Mining association rules between sets of items in large databases
    • 12,713
    • Highly Influential
    • PDF
    Maximizing Non-Monotone Submodular Functions
    • 235
    • Highly Influential
    • PDF