arXiv:physics/0408124physics.data-an
Boosted Decision Trees as an Alternative to Artificial Neural Networks for Particle Identification
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Abstract
The efficacy of particle identification is compared using artificial neutral networks and boosted decision trees. The comparison is performed in the context of the MiniBooNE, an experiment at Fermilab searching for neutrino oscillations. Based on studies of Monte Carlo samples of simulated data, particle identification with boosting algorithms has better performance than that with artificial neural networks for the MiniBooNE experiment. Although the tests in this paper were for one experiment, it is expected that boosting algorithms will find wide application in physics.
> I have yet to see a real world production pipeline where GBDT provides enough improvement over Logistic Regression Not my field at all, so "I know nooothing". Are GBDT's very different from "plain" binary decision trees? I've seen the latter a lot in the context of particle…
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