NCART: Neural Classification and Regression Tree for Tabular Data

TL;DR

NCART introduces an interpretable neural network combining decision trees with residual networks, offering efficient, scalable, and accurate analysis of tabular data across various dataset sizes.

Contribution

The paper presents NCART, a novel neural network architecture that integrates differentiable decision trees into residual networks, enhancing interpretability and reducing computational costs for tabular data analysis.

Findings

NCART outperforms existing deep learning models in accuracy.

NCART maintains interpretability while being computationally efficient.

Suitable for datasets of varying sizes.

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 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.