期刊
JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS
卷 47, 期 8, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1361-6471/ab92e3
关键词
statistical methods; machine learning; nuclear mass models; binding energy; decision tree
资金
- STFC [ST/P003885/1]
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.
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