Co-Founding Partner of Interpretable AI
Jack Dunn is a Co-Founding Partner of Interpretable AI. He has developed a number of novel machine learning methods including the award-winning Optimal Decision Trees. He has extensive experience applying machine learning, AI, and optimization to problems in a wide range of industries including health care, banking, insurance, and information technology. He has published papers in top journals, and is currently working on two textbooks in machine learning and its applications in health care. He was previously in software engineering at Google, and has led the development of many popular open-source projects. Jack has a PhD in Operations Research from MIT.
Track: Artificial Intelligence & Healthcare
Monday, April 15, 9:10–10:00am
Optimal Classification and Regression Trees
For the past 30 years, decision tree methods have been one of the most widely used approaches in machine learning across industry and academia, due in large part to their interpretability. However, this interpretability comes at a price—the performance of classical decision tree methods is typically not competitive with state of-the-art methods like random forests, boosted trees, and neural networks. We present Optimal Classification and Regression Trees, a novel method that leverages the improvements in optimization over the past three decades to produce decision trees that deliver interpretability and state-of-the-art performance simultaneously. We show comprehensive evidence that this method is tractable and performs competitively with random forests, boosting and neural networks. We also show how the interpretability of these trees has led to transformational business impact with a variety of cases in healthcare, insurance, financial services, cybersecurity, and more.