Summary of Squeezing Lemons with Hammers: An Evaluation Of Automl and Tabular Deep Learning For Data-scarce Classification Applications, by Ricardo Knauer et al.
Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applications
by Ricardo Knauer, Erik Rodner
First submitted to arxiv on: 13 May 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 In this paper, researchers investigate the effectiveness of various machine learning approaches in low-data regimes, where only small amounts of tabular data are available. They find that a simple approach, L2-regularized logistic regression, performs similarly well as more complex methods like meta-learning and ensembling on most datasets. This suggests that logistic regression could be a good starting point for many data-scarce applications, and provides guidelines for practitioners to select the best method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how different machine learning techniques do when you only have a little bit of data. They tested many different approaches on small datasets and found that a simple one called logistic regression did almost as well as more complicated ones. This means that if you’re working with very little data, you might want to try using logistic regression first. |
Keywords
» Artificial intelligence » Logistic regression » Machine learning » Meta learning
