Why In-Context Learning Transformers are Tabular Data Classifiers

• URL: http://arxiv.org/abs/2405.13396v1
• Date: Wed, 22 May 2024 07:13:55 GMT
• Title: Why In-Context Learning Transformers are Tabular Data Classifiers
• Authors: Felix den Breejen, Sangmin Bae, Stephen Cha, Se-Young Yun,
• Abstract summary: We show that ICL-transformers acquire the ability to create complex decision boundaries during pretraining. We create TabForestPFN, the ICL-transformer pretrained on both the original TabPFN synthetic dataset generator and our forest dataset generator.
• Score: 22.33649426762373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recently introduced TabPFN pretrains an In-Context Learning (ICL) transformer on synthetic data to perform tabular data classification. As synthetic data does not share features or labels with real-world data, the underlying mechanism that contributes to the success of this method remains unclear. This study provides an explanation by demonstrating that ICL-transformers acquire the ability to create complex decision boundaries during pretraining. To validate our claim, we develop a novel forest dataset generator which creates datasets that are unrealistic, but have complex decision boundaries. Our experiments confirm the effectiveness of ICL-transformers pretrained on this data. Furthermore, we create TabForestPFN, the ICL-transformer pretrained on both the original TabPFN synthetic dataset generator and our forest dataset generator. By fine-tuning this model, we reach the current state-of-the-art on tabular data classification. Code is available at https://github.com/FelixdenBreejen/TabForestPFN.

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