Summary of A Closer Look at Deep Learning Methods on Tabular Datasets, by Han-jia Ye et al.
A Closer Look at Deep Learning Methods on Tabular Datasets
by Han-Jia Ye, Si-Yang Liu, Hao-Run Cai, Qi-Le Zhou, De-Chuan Zhan
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an in-depth evaluation and comprehensive analysis of tabular data methods, focusing on Deep Neural Network (DNN)-based models. The authors compare 32 state-of-the-art deep and tree-based methods across over 300 datasets, covering various task types, sizes, and domains. They find that top-performing methods tend to concentrate within a small subset of tabular models regardless of the evaluation criteria used. Additionally, they investigate whether training dynamics of DNN-based tabular models can be predicted based on dataset properties, identifying “meta-features” that reflect dataset heterogeneity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how different machine learning methods work with tables of data. It compares many different methods and sees which ones do best across lots of different types of datasets. They found that the top-performing methods are often similar, no matter what kind of data they’re working with. They also tried to figure out if they could predict how well a method would work by looking at the properties of the data it’s trying to learn from. |
Keywords
» Artificial intelligence » Machine learning » Neural network
