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Found 3 papers, 1 papers with code
Learning Optimal Classification Trees Robust to Distribution Shifts
We consider the problem of learning classification trees that are robust to distribution shifts between training and testing/deployment data.
ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription
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 mixed-integer optimization (MIO) framework proposed in Aghaei et al. (2019) and several of its extensions.
Optimal Robust Classification Trees
In many high-stakes domains, the data used to drive machine learning algorithms is noisy (due to e. g., the sensitive nature of the data being collected, limited resources available to validate the data, etc).
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