Summary of A Comprehensive Benchmark Of Machine and Deep Learning Across Diverse Tabular Datasets, by Assaf Shmuel et al.
A Comprehensive Benchmark of Machine and Deep Learning Across Diverse Tabular Datasets
by Assaf Shmuel, Oren Glickman, Teddy Lazebnik
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces a comprehensive benchmark for analyzing tabular datasets using Machine Learning (ML) models, focusing on identifying the types of datasets where Deep Learning (DL) models excel. The benchmark evaluates 111 datasets with 20 different models, including both regression and classification tasks, featuring varying scales and categorical variables. Notably, the dataset includes scenarios where DL models outperform alternative methods, enabling a thorough analysis of conditions under which DL models succeed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper helps us understand when deep learning models are better than other machine learning techniques for analyzing tables of data. It compares many different types of datasets and ML models to see when deep learning is the best choice. The results can help scientists and researchers choose the right approach for their specific problem. |
Keywords
» Artificial intelligence » Classification » Deep learning » Machine learning » Regression
