TabRet: Pre-training Transformer-based Tabular Models for Unseen Columns

• URL: http://arxiv.org/abs/2303.15747v4
• Date: Sun, 16 Apr 2023 03:42:52 GMT
• Title: TabRet: Pre-training Transformer-based Tabular Models for Unseen Columns
• Authors: Soma Onishi, Kenta Oono, and Kohei Hayashi
• Abstract summary: TabRet is designed to work on a downstream task that contains columns not seen in pre-training. In experiments, we pre-trained TabRet with a large collection of public health surveys and fine-tuned it on classification tasks in healthcare. In addition, an ablation study shows retokenizing and random shuffle augmentation of columns during pre-training contributed to performance gains.
• Score: 12.139158398361866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present \emph{TabRet}, a pre-trainable Transformer-based model for tabular data. TabRet is designed to work on a downstream task that contains columns not seen in pre-training. Unlike other methods, TabRet has an extra learning step before fine-tuning called \emph{retokenizing}, which calibrates feature embeddings based on the masked autoencoding loss. In experiments, we pre-trained TabRet with a large collection of public health surveys and fine-tuned it on classification tasks in healthcare, and TabRet achieved the best AUC performance on four datasets. In addition, an ablation study shows retokenizing and random shuffle augmentation of columns during pre-training contributed to performance gains. The code is available at https://github.com/pfnet-research/tabret .

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