20.500.12556/RUL-175824 Predicting equine behavior from small datasets using machine learning with LLM-generated explanations Understanding horse fearfulness is crucial for building strong human–animal relationships, influencing training methods, task selection, and predicting reactions to new stimuli. This interdisciplinary study aimed to identify key characteristics for predicting a horse’s fearfulness. Using classical machine learning, we analyzed anatomical, kinematic, and housing-related data from 49 horses, with fear scores obtained through a standardized behavioral test. To ensure an unbiased evaluation, the leave-one-out cross-validation method was applied. The study’s main contributions are: (1) an iterative feature selection approach that reduces the number of required measurements while maintaining prediction accuracy; (2) a unique dataset on Lipizzan horses, revealing that head and body anatomical characteristics are critical for assessing fearfulness; (3) identification of the Decision Tree algorithm as the most accurate machine learning method for modeling fearfulness; (4) integration of Large Language Models (LLMs) to generate clear, interpretable textual explanations of the Decision Tree, improving the understanding of key predictive features. This study bridges behavioral science and artificial intelligence, offering a novel AI-driven approach to equine behavior analysis, with practical applications in horse training, selection, and welfare management. machine learning Lipizzan horses data analysis horse fearfulness supervised learning horse welfare konji obnašanje živali etologija plašnost strojno učenje umetna inteligenca true false true Angleški jezik Slovenski jezik Članek v reviji 2025-11-10 12:22:12 2025-11-10 12:22:14 2026-06-15 10:34:43 0000-00-00 00:00:00 2025 0 0 13 str. art. 106863 Vol. 293 Dec. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 636.1:591.5 1872-9045 10.1016/j.applanim.2025.106863 256506115 RAZ_Topal_Oleksandra_2025.pdf RAZ_Topal_Oleksandra_2025.pdf 1 0063B91478C174F3DFAD1BB39F8C5707 e822fba793f929b5aa9120dc2b10eb0bfa310e4dd66869227481c1f364817428 4354ba8d-d992-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=223703 https://www.sciencedirect.com/science/article/pii/S0168159125003612 0 bc085808-ead2-11f0-bf92-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=225000 Biotehniška fakulteta 0 0 0