BORT: Beyond Pareto: Bi-Objective and Multi-Objective Regression
Trees’
Implements the Bi-objective Regression Tree (BORT) for efficiently
learning vector-valued functions. Unlike traditional methods that rely on
constructing multiple models or static scalarisation, BORT integrates the
exploration of the Pareto front directly into a single tree's growth process.
It provides high-efficiency, single-model approaches that can Pareto-dominate
entire Pareto-consistent families of trees, supported by a C backend for
fast computation. For more details see
Paz (2026) <doi:10.1007/978-3-032-28393-1_2> and
Paz (2025) <doi:10.1007/978-3-031-78401-9_2>.
[aut, cre],
Arturo Hernández-Aguirre