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Boosted decision trees

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arxiv 2206.09645 v1 pith:CK3PHGF6 submitted 2022-06-20 physics.data-an hep-ex

Boosted decision trees

classification physics.data-an hep-ex
keywords decisionlearningtreesboostedboostinghigh-energymachinephysics
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Boosted decision trees are a very powerful machine learning technique. After introducing specific concepts of machine learning in the high-energy physics context and describing ways to quantify the performance and training quality of classifiers, decision trees are described. Some of their shortcomings are then mitigated with ensemble learning, using boosting algorithms, in particular AdaBoost and gradient boosting. Examples from high-energy physics and software used are also presented.

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