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. © 2008.
- Decision tree