  Preference learning (PL) is a core area of machine learning that handles
datasets with ordinal relations. As the number of generated data of ordinal
nature is increasing, the importance and role of the PL field becomes central
within machine learning research and practice. This paper introduces an open
source, scalable, efficient and accessible preference learning toolbox that
supports the key phases of the data training process incorporating various
popular data preprocessing, feature selection and preference learning methods.
