Antoranz Canales, PedroBarrio Uña, Juan AbelContreras González, José LuisFonseca González, María VictoriaLópez Moya, MarcosMiranda Pantoja, José MiguelNieto Castaño, Daniel2023-06-202023-06-202008-04-110168-900210.1016/j.nima.2007.11.068https://hdl.handle.net/20.500.14352/50837© Elsevier Science.We thank Jens Zimmermann for fruitful discussions about the RF method and for comparisons of the RF method with a Neural Net approach.The paper describes an application of the tree classification method Random Forest (RF), as used in the analysis of data from the ground-based gamma telescope MAGIC. In such telescopes, cosmic gamma-rays are observed and have to be discriminated against a dominating background of hadronic cosmic-ray particles. We describe the application of RF for this gamma/hadron separation. The RF method often shows superior performance in comparison with traditional semi-empirical techniques. Critical issues of the method and its implementation are discussed. An application of the RF method for estimation of a continuous parameter from related variables, rather than discrete classes, is also discussed. (C) 2008 Published by Elsevier B.V.engjournal articlehttp://dx.doi.org/10.1016/j.nima.2007.11.068http://www.sciencedirect.comhttp://arxiv.org/abs/0709.3719open access537539.1Gamma-RaySeparationRadiation.Electrónica (Física)ElectricidadFísica nuclear2202.03 Electricidad2207 Física Atómica y Nuclear