Learning Physics from the Machine: An Interpretable Boosted Decision Tree Analysis for the Majorana Demonstrator.
I. J. ArnquistF. T. Avignone IIIA. S. BarabashC. J. BartonK. H. BhimaniE. BlalockB. BosM. BuschM. BuuckThomas S. CaldwellY.-D. ChanC. D. ChristoffersonP.-H. ChuM. L. ClarkC. CuestaJ. A. DetwilerYu. EfremenkoS. R. ElliottG. K. GiovanettiM. P. GreenJ. GruszkoI. S. GuinnV. E. GuiseppeC. R. HaufeR. HenningD. Hervas AguilarE. W. HoppeA. HostiucM. F. KiddI. KimR. T. KouzesT. E. Lannen VA. LiJ. M. Lopez-CastanoE. L. MartinR. D. MartinR. MassarczykS. J. MeijerT. K. OliG. OthmanL. S. PaudelW. PettusA. W. P. PoonD. C. RadfordA. L. ReineK. RielageN. W. RuofD. C. SchaperD. TedeschiR. L. VarnerS. VasilyevJ. F. WilkersonC. WisemanW. XuC.-H. YuPublished in: CoRR (2022)
Keyphrases
- learning process
- decision trees
- background knowledge
- neural network
- inductive learning
- active learning
- learning systems
- logistic regression
- decision tree learning
- data sets
- constructive induction
- learning machines
- incremental learning
- learning problems
- mobile learning
- statistical analysis
- supervised learning
- training set
- computer science
- reinforcement learning
- machine learning