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Found 4 papers, 3 papers with code
Optimal Decision Trees for Separable Objectives: Pushing the Limits of Dynamic Programming
However, many of the methods used rely on general-purpose solvers for which scalability remains an issue.
Optimal Decision Trees for Nonlinear Metrics
Nonlinear metrics, such as the F1-score, Matthews correlation coefficient, and Fowlkes-Mallows index, are often used to evaluate the performance of machine learning models, in particular, when facing imbalanced datasets that contain more samples of one class than the other.
MurTree: Optimal Classification Trees via Dynamic Programming and Search
Heuristic methods are traditionally used to quickly produce models with reasonably high accuracy.
Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems
Recently, Smart Predict and Optimize (SPO) has been proposed for problems with a linear objective function over the predictions, more specifically linear programming problems.
