Self-organizing maps can encode tabular data more effectively than simpler dimensionality reduction by capturing feature relationships alongside values, improving both predictive performance and model interpretability.
TabSOM converts tabular data into images using self-organizing maps, preserving both feature values and relationships between features. Unlike existing methods that only encode individual feature values, TabSOM captures feature interactions as spatial patterns, enabling vision models to achieve better performance while remaining interpretable.