A Comparative Analysis of Decision Trees, Neural Networks, and Bayesian Networks: Methodological Insights and Practical Applications in Machine Learning

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 domainspecific
challenges.