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Optimal Sparse Decision Trees

dc.contributor.advisor Rudin, Cynthia
dc.contributor.advisor Reiter, Jerome Hu, Xiyang 2019-06-07T19:51:24Z 2019-06-07T19:51:24Z 2019
dc.description Master's thesis
dc.description.abstract <p>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.</p>
dc.subject Computer science
dc.subject Statistics
dc.subject Operations research
dc.subject Decision trees
dc.subject Interpretable models
dc.subject Optimization
dc.title Optimal Sparse Decision Trees
dc.type Master's thesis
dc.department Statistical Science

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