Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applications
<|reference_start|>Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applications: Many industry verticals are confronted with small-sized tabular data. In this low-data regime, it is currently unclear whether the best performance can be expected from simple baselines, or more complex machine learning approaches that leverage meta-learning and ensembling. On 44 tabular classification datasets with sample sizes $\leq$ 500, we find that L2-regularized logistic regression performs similar to state-of-the-art automated machine learning (AutoML) frameworks (AutoPrognosis, AutoGluon) and off-the-shelf deep neural networks (TabPFN, HyperFast) on the majority of the benchmark datasets. We therefore recommend to consider logistic regression as the first choice for data-scarce applications with tabular data and provide practitioners with best practices for further method selection.<|reference_end|>
@article{knauer2024squeezing,
    title={Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep
  Learning for Data-Scarce Classification Applications},
    author={Ricardo Knauer, Erik Rodner},
    journal={arXiv preprint arXiv:2405.07662},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07662},
    primaryClass={cs.LG cs.AI}
}