Comparative Analysis of Tree-Based Models and Deep Learning Architectures for Tabular Data: Performance Disparities and Underlying Factors

This research paper undertakes a comparative analysis of tree-based models and deep learning architectures concerning their performance disparities in handling tabular data. Tree-based models, known for their interpretability and robustness, are juxtaposed with deep learning architectures, characterized by intricate, interconnected neural networks. The study delves into fundamental constructs, elucidating the essence of decision trees, ensemble methods, and neural networks. Performance disparities are meticulously dissected, with a keen focus on underlying factors such as data complexity, dimensionality, and inherent patterns. Visual aids, diagrams, and mathematical formulas are employed to facilitate a comprehensive understanding, catering to both seasoned practitioners and newcomers in the domain of data science and machine learning. Through this comparative analysis, the research seeks to shed light on the strengths and weaknesses of these modelling approaches, aiding informed decision-making in data analysis and model selection.