Studies of Boosted Decision Trees for MiniBooNE Particle Identification
Hai-Jun Yang, Byron P. Roe, Ji Zhu
Abstract
Boosted decision trees are applied to particle identification in the MiniBooNE experiment operated at Fermi National Accelerator Laboratory (Fermilab) for neutrino oscillations. Numerous attempts are made to tune the boosted decision trees, to compare performance of various boosting algorithms, and to select input variables for optimal performance.
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