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Found 5 papers, 5 papers with code
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.
MurTree: Optimal Classification Trees via Dynamic Programming and Search
Heuristic methods are traditionally used to quickly produce models with reasonably high accuracy.
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.
Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming
However, many of the methods used rely on general-purpose solvers for which scalability remains an issue.
Optimal Survival Trees: A Dynamic Programming Approach
Survival analysis studies and predicts the time of death, or other singular unrepeated events, based on historical data, while the true time of death for some instances is unknown.
