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20182026
most citedLearning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracy

23 citations · 70 across the 12 of their papers we have counts for

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Showing 2023Show all

2 papers · 1 filter

cs.LG2023★ 1 cited

Learning Optimal Classification Trees Robust to Distribution Shifts

Nathan Justin, Sina Aghaei, Andrés Gómez +1

We consider the problem of learning classification trees that are robust to distribution shifts between training and testing/deployment data. This problem arises frequently in high…

stat.ML2023★ 1 cited

ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription

Patrick Vossler, Nathan Justin, Sina Aghaei +3

ODTlearn is an open source Python package that provides methods for learning optimal decision trees for high-stakes predictive and prescriptive tasks based on the state-of-the-art…