NCART: Neural Classification and Regression Tree for Tabular Data

arXiv:2307.12198v2 Announce Type: replace
Abstract: Deep learning models have become popular in the analysis of tabular data, as they address the limitations of decision trees and enable valuable applications like semi-supervised learning, online learning, and transfer learning. However, these deep-learning approaches often encounter a trade-off. On one hand, they can be computationally expensive when dealing with large-scale or high-dimensional datasets. On the other hand, they may lack interpretability and may not be suitable for small-scale datasets. In this study, we …

abstract analysis applications arxiv become classification cs.lg data decision decision trees deep learning limitations online learning popular regression semi-supervised semi-supervised learning supervised learning tabular tabular data trade trade-off transfer transfer learning tree trees type