Trees and Forests in Nuclear Physics

02/24/2020
by   Marco Carnini, et al.
0

We present a detailed introduction to the decision tree algorithm using some simple examples taken from the domain of nuclear physics. We show how to improve the accuracy of the classical liquid drop nuclear mass model by performing Feature Engineering while using a decision tree. Finally, we apply the method to the Duflo-Zucker mass model showing that, despite their simplicity, decision trees are capable of obtaining a level of accuracy comparable to more complex neural networks, but using way less adjustable parameters and obtaining easier to explain models.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/13/2014

The New Approach on Fuzzy Decision Trees

Decision trees have been widely used in machine learning. However, due t...
research
03/10/2020

Interval Temporal Logic Decision Tree Learning

Decision trees are simple, yet powerful, classification models used to c...
research
07/23/2019

Trees and Islands -- Machine learning approach to nuclear physics

We implement machine learning algorithms to nuclear data. These algorith...
research
03/29/2019

PAC Learnability of nuclear masses

After more than 80 years from the seminal work of Weizsäcker and the liq...
research
11/11/2022

An introduction to computational complexity and statistical learning theory applied to nuclear models

The fact that we can build models from data, and therefore refine our mo...
research
12/19/2017

A Faster Drop-in Implementation for Leaf-wise Exact Greedy Induction of Decision Tree Using Pre-sorted Deque

This short article presents a new implementation for decision trees. By ...
research
06/12/2017

Random Forests, Decision Trees, and Categorical Predictors: The "Absent Levels" Problem

One of the advantages that decision trees have over many other models is...

Please sign up or login with your details

Forgot password? Click here to reset