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>.
| Version: |
0.1.0 |
| Depends: |
R (≥ 2.10.0) |
| Published: |
2026-07-07 |
| DOI: |
10.32614/CRAN.package.BORT |
| Author: |
Erick G.G. de Paz 
[aut, cre],
Arturo Hernández-Aguirre
[aut],
Iván Cruz-Aceves
[aut] |
| Maintainer: |
Erick G.G. de Paz <erick.giles at cimat.mx> |
| License: |
GPL-2 |
| NeedsCompilation: |
yes |
| CRAN checks: |
BORT results |
Documentation:
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