Generalized and Scalable Optimal Sparse Decision Trees

Rudin, Cynthia

Zhong, Chudi

2020-06-09T17:45:23Z

2020-12-01T09:17:12Z

2020

Statistical Science

Decision tree optimization is notoriously difficult from a computational perspective but essential for the field of interpretable machine learning. Despite efforts over the past 40 years, only recently have optimization breakthroughs been made that have allowed practical algorithms to find \textit{optimal} decision trees. These new techniques have the potential to trigger a paradigm shift where it is possible to construct sparse decision trees to efficiently optimize a variety of objective functions without relying on greedy splitting and pruning heuristics that often lead to suboptimal solutions. The contribution in this work is to provide a general framework for decision tree optimization that addresses the two significant open problems in the area: treatment of imbalanced data and fully optimizing over continuous variables. We present techniques that produce optimal decision trees over a variety of objectives including F-score, AUC, and partial area under the ROC convex hull. We also introduce a scalable algorithm that produces provably optimal results in the presence of continuous variables and speeds up decision tree construction by several orders of magnitude relative to the state-of-the art.

https://hdl.handle.net/10161/20774

Statistics

Computer science

Decision trees

Interpretable Model

Operations research

Optimization

Generalized and Scalable Optimal Sparse Decision Trees

Master's thesis

5.7534246575342465