Trees and forests in nuclear physics

被引:23
作者
Carnini, M. [1 ]
Pastore, A. [2 ]
机构
[1] Features Analyt, Rue Charleroi 2, B-1400 Nivelles, Belgium
[2] Univ York, Dept Phys, York YO10 5DD, N Yorkshire, England
基金
英国科学技术设施理事会;
关键词
statistical methods; machine learning; nuclear mass models; binding energy; decision tree; BOOSTED DECISION TREES;
D O I
10.1088/1361-6471/ab92e3
中图分类号
O57 [原子核物理学、高能物理学];
学科分类号
070202 ;
摘要
We present a simple introduction to the decision tree algorithm using some examples from nuclear physics. We show how to improve the accuracy of the classical liquid drop nuclear mass model by performing feature engineering with a decision tree. Finally, we apply the method to the Duflo-Zuker model showing that, despite their simplicity, decision trees are capable of improving the description of nuclear masses using a limited number of free parameters.
引用
收藏
页数:22
相关论文
共 40 条
[1]  
[Anonymous], THESIS
[2]  
Barlow R. J., 1993, Statistics: A Guide to the Use of Statistical Methods in the Physical Sciences, V29
[3]   SCIENCE AND STATISTICS [J].
BOX, GEP .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1976, 71 (356) :791-799
[4]  
Boz O., 2002, P 8 ACM SIGKDD INT C, P456, DOI 10.1145/775047.775113
[5]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[6]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[7]   Statistical modeling: The two cultures [J].
Breiman, L .
STATISTICAL SCIENCE, 2001, 16 (03) :199-215
[8]  
Breiman L., 1984, wadsworth int. Group, DOI [DOI 10.1785/0120150058, DOI 10.1201/9781315139470]
[9]   XGBoost: A Scalable Tree Boosting System [J].
Chen, Tianqi ;
Guestrin, Carlos .
KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, :785-794
[10]   MICROSCOPIC MASS FORMULAS [J].
DUFLO, J ;
ZUKER, AP .
PHYSICAL REVIEW C, 1995, 52 (01) :R23-R27