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Optimal Sparse Decision Trees

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Date
2019
Author
Hu, Xiyang
Advisors
Rudin, Cynthia
Reiter, Jerome
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Abstract

Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980's. The problem that has plagued decision tree algorithms since their inception is their lack of optimality, or lack of guarantees of closeness to optimality: decision tree algorithms are often greedy or myopic, and sometimes produce unquestionably suboptimal models. Hardness of decision tree optimization is both a theoretical and practical obstacle, and even careful mathematical programming approaches have not been able to solve these problems efficiently. This work introduces the first practical algorithm for optimal decision trees for binary variables. The algorithm is a co-design of analytical bounds that reduce the search space and modern systems techniques, including data structures and a custom bit-vector library. We highlight possible steps to improving the scalability and speed of future generations of this algorithm based on insights from our theory and experiments.

Description
Master's thesis
Type
Master's thesis
Department
Statistical Science
Subject
Computer science
Statistics
Operations research
Decision trees
Interpretable models
Optimization
Permalink
https://hdl.handle.net/10161/18915
Citation
Hu, Xiyang (2019). Optimal Sparse Decision Trees. Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/18915.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.

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