TL;DR: authors propose a DenseNet-like ensemble of decision trees, trained end-to-end by backpropagation and beats both xgboost and neural networks on heterogeneous (“tabular”) data.

(IMHO) unlike all other “neural decision tree” methods this one worked out of the box for production scale problems without heavy wizardry.

Differentiable decision tree (figure 1 from arxiv paper)

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ArXiv: https://arxiv.org/abs/1909.06312

Source code: https://github.com/Qwicen/node

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Abstract:

Nowadays, deep neural networks (DNNs) have become the main instrument for machine learning tasks within a wide range of domains, including vision, NLP, and speech. Meanwhile, in an important case of heterogenous tabular data, the advantage of DNNs over shallow counterparts remains questionable. In particular, there is no sufficient evidence that deep learning machinery allows constructing methods that outperform gradient boosting decision trees (GBDT), which are often the top choice for tabular problems. In this paper, we introduce Neural Oblivious Decision Ensembles (NODE), a new deep learning architecture, designed to work with any tabular data. In a nutshell, the proposed NODE architecture generalizes ensembles of oblivious decision trees, but benefits from both end-to-end gradient-based optimization and the power of multi-layer hierarchical representation learning. With an extensive experimental comparison to the leading GBDT packages on a large number of tabular datasets, we demonstrate the advantage of the proposed NODE architecture, which outperforms the competitors on most of the tasks. We open-source the PyTorch implementation of NODE and believe that it will become a universal framework for machine learning on tabular data.