2014 AlgorithmsforInterpretableMachi

• (Rudin, 2014) ⇒ Cynthia Rudin. (2014). “Algorithms for Interpretable Machine Learning.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2630823

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Cited By

• http://scholar.google.com/scholar?q=%222014%22+Algorithms+for+Interpretable+Machine+Learning
• http://dl.acm.org/citation.cfm?id=2623330.2630823&preflayout=flat#citedby

Quotes

Author Keywords

• Comprehensibility; human factors; interpretability; knowledge acquisition; machine learning; sparsity, medical calculators; understandability

Abstract

It is extremely important in many application domains to have transparency in predictive modeling. Domain experts do not tend to prefer " black box " predictive models. They would like to understand how predictions are made, and possibly, prefer models that emulate the way a human expert might make a decision, with a few important variables, and a clear convincing reason to make a particular prediction.

I will discuss recent work on interpretable predictive modeling with decision lists and sparse integer linear models. I will describe several approaches, including an algorithm based on discrete optimization, and an algorithm based on Bayesian analysis. I will show examples of interpretable models for stroke prediction in medical patients and prediction of violent crime in young people raised in out-of-home care.

Collaborators are Ben Letham, Berk Ustun, Stefano Traca, Siong Thye Goh, Tyler McCormick, and David Madigan.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 AlgorithmsforInterpretableMachi	Cynthia Rudin			Algorithms for Interpretable Machine Learning				10.1145/2623330.2630823		2014