🔗 The TOC-UCO repository can be downloaded from this website. The following bash command can also be executed:
wget https://www.uco.es/grupos/ayrna/datasets/TOC-UCO.zip📕 In the tutorial notebook you can find the code on how to load the train-test partitions of each TOC-UCO dataset.
📘 In the baseline experiments script, the code for executing different methodologies in the TOC-UCO repository is provided.
💻 More about ordinal classification can be found in the dlordinal package, where several ordinal techniques are implemented, together with the most popular ordinal performance metrics.
📚 If you enjoyed this repository, we would appretiate a citation for the following work: If you enjoyed this framework, we would appretiate a citation for the following work:
@article{Ayllon2026TOC,
author = {Ayll{\' o}n-Gavil{\' a}n, Rafael and Guijo-Rubio, David and G{\' o}mez-Orellana, Antonio Manuel and B{\' e}rchez-Moreno, Francisco and Vargas-Yun, V{\' i}ctor Manuel and Guti{\' e}rrez, Pedro Antonio},
journal = {Neurocomputing},
doi = {10.1016/j.neucom.2026.133528},
year = {2026},
pages = {133528},
title = {TOC-{UCO}: a comprehensive repository of tabular ordinal classification datasets},
url = {https://www.sciencedirect.com/science/article/pii/S0925231226009252},
howpublished = {https://www.sciencedirect.com/science/article/pii/S0925231226009252},
}