PreprintArticleVersion 1This version is not peer-reviewed
A Comparative Analysis of Decision Trees, Neural Networks, and Bayesian Networks: Methodological Insights and Practical Applications in Machine Learning
Version 1
: Received: 18 October 2024 / Approved: 18 October 2024 / Online: 19 October 2024 (08:50:43 CEST)
How to cite:
Montgomery, R. M. A Comparative Analysis of Decision Trees, Neural Networks, and Bayesian Networks: Methodological Insights and Practical Applications in Machine Learning. Preprints2024, 2024101491. https://doi.org/10.20944/preprints202410.1491.v1
Montgomery, R. M. A Comparative Analysis of Decision Trees, Neural Networks, and Bayesian Networks: Methodological Insights and Practical Applications in Machine Learning. Preprints 2024, 2024101491. https://doi.org/10.20944/preprints202410.1491.v1
Montgomery, R. M. A Comparative Analysis of Decision Trees, Neural Networks, and Bayesian Networks: Methodological Insights and Practical Applications in Machine Learning. Preprints2024, 2024101491. https://doi.org/10.20944/preprints202410.1491.v1
APA Style
Montgomery, R. M. (2024). A Comparative Analysis of Decision Trees, Neural Networks, and Bayesian Networks: Methodological Insights and Practical Applications in Machine Learning. Preprints. https://doi.org/10.20944/preprints202410.1491.v1
Chicago/Turabian Style
Montgomery, R. M. 2024 "A Comparative Analysis of Decision Trees, Neural Networks, and Bayesian Networks: Methodological Insights and Practical Applications in Machine Learning" Preprints. https://doi.org/10.20944/preprints202410.1491.v1
Abstract
This paper provides an in-depth comparative analysis of three prominent machine learning techniques: decision trees, neural networks, and Bayesian networks. Each method is explored in terms of its theoretical foundations, algorithmic structure, strengths, limitations, and real-world applications. Decision trees are celebrated for their simplicity and interpretability, making them ideal for decision-making systems, but they often struggle with overfitting and poor performance on high-dimensional data. Neural networks, while capable of achieving high accuracy and effectively handling complex, non-linear patterns, are criticized for their "black-box" nature and computational intensity. Bayesian networks distinguish themselves through their ability to model uncertainty and incorporate prior knowledge, making them highly applicable in scenarios requiring probabilistic reasoning, yet they are challenging to scale for complex, high-dimensional data sets. This comparative analysis highlights the distinctive advantages of each method, their performance across various domains such as healthcare, finance, and risk assessment, and the growing potential of hybrid models that combine the strengths of these techniques. The paper concludes by discussing future research opportunities, particularly in enhancing model interpretability and scalability while addressing domain-specific challenges.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.