NCART: Neural Classification and Regression Tree for Tabular Data
This addresses the problem of balancing efficiency and interpretability for researchers and practitioners working with tabular datasets of varying sizes, though it appears incremental as it builds on existing neural network and decision tree concepts.
The paper tackled the trade-off between computational cost and interpretability in deep learning for tabular data by proposing NCART, a neural network with differentiable oblivious decision trees, which achieved superior performance compared to existing deep learning models.
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 propose a novel interpretable neural network called Neural Classification and Regression Tree (NCART) to overcome these challenges. NCART is a modified version of Residual Networks that replaces fully-connected layers with multiple differentiable oblivious decision trees. By integrating decision trees into the architecture, NCART maintains its interpretability while benefiting from the end-to-end capabilities of neural networks. The simplicity of the NCART architecture makes it well-suited for datasets of varying sizes and reduces computational costs compared to state-of-the-art deep learning models. Extensive numerical experiments demonstrate the superior performance of NCART compared to existing deep learning models, establishing it as a strong competitor to tree-based models.