Communications for Statistical Applications and Methods

  • 제30권2호
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  • Pages.215-226
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  • 2023
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Recent deep learning methods for tabular data

  • Yejin Hwang (Department of Statistics, Ewha Womans University) ;
  • Jongwoo Song (Department of Statistics, Ewha Womans University)
  • 투고 : 2022.10.06
  • 심사 : 2022.12.19
  • 발행 : 2023.03.31
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초록

Deep learning has made great strides in the field of unstructured data such as text, images, and audio. However, in the case of tabular data analysis, machine learning algorithms such as ensemble methods are still better than deep learning. To keep up with the performance of machine learning algorithms with good predictive power, several deep learning methods for tabular data have been proposed recently. In this paper, we review the latest deep learning models for tabular data and compare the performances of these models using several datasets. In addition, we also compare the latest boosting methods to these deep learning methods and suggest the guidelines to the users, who analyze tabular datasets. In regression, machine learning methods are better than deep learning methods. But for the classification problems, deep learning methods perform better than the machine learning methods in some cases.

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