Variational oblique predictive clustering treesAndonovikj, Viktor (Avtor) Džeroski, Sašo (Avtor) Boshkoska, Biljana Mileva (Avtor) Boškoski, Pavle (Avtor) machine learningpredictive clusteringinterpretable modelsstructured output predictionuncertainty quantificationOblique predictive clustering trees (SPYCTs) are semi-supervised multi-target prediction models mainly used for structured output prediction (SOP) problems. They are computationally efficient and when combined in ensembles they achieve state-of-the-art results. However, one major issue is that it is challenging to interpret an ensemble of SPYCTs without the use of a model-agnostic method. We propose variational oblique predictive clustering trees, which address this challenge. The parameters of each split node are treated as random variables, described with a probability distribution, and they are learned through the Variational Bayes method. We evaluate the model on several benchmark datasets of different sizes. The experimental analyses show that a single variational oblique predictive clustering tree (VSPYCT) achieves competitive, and sometimes better predictive performance than the ensemble of standard SPYCTs. We also present a method for extracting feature importance scores from the model. Finally, we present a method to visually interpret the model’s decision making process through analysis of the relative feature importance in each split node.Elsevier20262026-02-17 11:39:54Neznano27644UDK: 004.8ISSN pri članku: 1873-6793DOI: 10.1016/j.eswa.2026.131255COBISS_ID: 266512899Nizozemskasl© 2026 The Author(s).