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Abstract
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 DufloZuker model showing that, despite their simplicity, decision trees are capable of improving the
description of nuclear masses using a limited number of free parameters
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 DufloZuker model showing that, despite their simplicity, decision trees are capable of improving the
description of nuclear masses using a limited number of free parameters
| Original language | English |
|---|---|
| Pages (from-to) | 1-23 |
| Number of pages | 23 |
| Journal | Journal of physics g-Nuclear and particle physics |
| Early online date | 8 Jul 2020 |
| Publication status | E-pub ahead of print - 8 Jul 2020 |
Bibliographical note
© 2020, The Author(s).Projects
- 1 Finished
-
Nuclear Physics Theory
Dobaczewski, J. J. & Pastore, A.
SCIENCE AND TECHNOLOGY FACILITIES COUNCIL (STFC)
1/06/15 → 31/03/20
Project: Research project (funded) › Research