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Computer Science > Machine Learning

arXiv:2207.08815 (cs)
[Submitted on 18 Jul 2022]

Title:Why do tree-based models still outperform deep learning on
tabular data?

Authors:Leo Grinsztajn (SODA), Edouard Oyallon (ISIR, CNRS), Gael
Varoquaux (SODA)
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    Abstract: While deep learning has enabled tremendous progress on
    text and image datasets, its superiority on tabular data is not
    clear. We contribute extensive benchmarks of standard and novel
    deep learning methods as well as tree-based models such as
    XGBoost and Random Forests, across a large number of datasets and
    hyperparameter combinations. We define a standard set of 45
    datasets from varied domains with clear characteristics of
    tabular data and a benchmarking methodology accounting for both
    fitting models and finding good hyperparameters. Results show
    that tree-based models remain state-of-the-art on medium-sized
    data ($\sim$10K samples) even without accounting for their
    superior speed. To understand this gap, we conduct an empirical
    investigation into the differing inductive biases of tree-based
    models and Neural Networks (NNs). This leads to a series of
    challenges which should guide researchers aiming to build
    tabular-specific NNs: 1. be robust to uninformative features, 2.
    preserve the orientation of the data, and 3. be able to easily
    learn irregular functions. To stimulate research on tabular
    architectures, we contribute a standard benchmark and raw data
    for baselines: every point of a 20 000 compute hours
    hyperparameter search for each learner.

Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI);
          Methodology (stat.ME); Machine Learning (stat.ML)
Cite as:  arXiv:2207.08815 [cs.LG]
          (or arXiv:2207.08815v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2207.08815
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Leo Grinsztajn [view email] [via CCSD proxy]
[v1] Mon, 18 Jul 2022 08:36:08 UTC (20,916 KB)
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