Gómez López, Antón2026-06-012026-06-012024-07https://hdl.handle.net/10347/4745366 páxsNeste traballo analízanse diversas técnicas de ensamblado en aprendizaxe supervisada, enfocándose en bagging, bosques aleatorios e adaBoost. Inicialmente, explícanse os fundamentos da clasificación estatística e da aprendizaxe supervisada. Seguidamente, examínanse as diferentes estratexias para combinar saídas de clasificadores cando estas consisten en predicións e valores continuos. Finalmente, detállanse os métodos de ensamblado, subliñando as características que os diferencianIn this work, various ensemble techniques in supervised learning are analyzed, focusing on bagging, random forests, and adaBoost. Initially, the fundamentals of statistical classification and supervised learning are explained. Then, the different strategies for combining classifier outputs, consisting of predictions and continuous values, are examined. Finally, the ensemble methods are detailed, highlighting the characteristics that differentiate them.glgAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/bachelor thesisopen access