TransTab: Learning Transferable Tabular Transformers Across Tables

TL;DR

TransTab introduces a transferable transformer model for tabular data that handles varying table structures, enabling better generalization, incremental learning, and transfer learning across diverse datasets with minimal data preprocessing.

Contribution

The paper presents TransTab, a novel transformer-based approach that learns transferable embeddings from tabular data, accommodating varying table structures and enabling pretraining and incremental updates.

Findings

TransTab outperforms 11 baseline methods across multiple benchmarks.

Pretraining with TransTab improves AUC by 2.3% on average.

TransTab effectively handles unseen tables with different columns.

Abstract

Tabular data (or tables) are the most widely used data format in machine learning (ML). However, ML models often assume the table structure keeps fixed in training and testing. Before ML modeling, heavy data cleaning is required to merge disparate tables with different columns. This preprocessing often incurs significant data waste (e.g., removing unmatched columns and samples). How to learn ML models from multiple tables with partially overlapping columns? How to incrementally update ML models as more columns become available over time? Can we leverage model pretraining on multiple distinct tables? How to train an ML model which can predict on an unseen table? To answer all those questions, we propose to relax fixed table structures by introducing a Transferable Tabular Transformer (TransTab) for tables. The goal of TransTab is to convert each sample (a row in the table) to a generalizable embedding vector, and then apply stacked transformers for feature encoding. One methodology insight is combining column description and table cells as the raw input to a gated transformer model. The other insight is to introduce supervised and self-supervised pretraining to improve model performance. We compare TransTab with multiple baseline methods on diverse benchmark datasets and five oncology clinical trial datasets. Overall, TransTab ranks 1.00, 1.00, 1.78 out of 12 methods in supervised learning, feature incremental learning, and transfer learning scenarios, respectively; and the proposed pretraining leads to 2.3% AUC lift on average over the supervised learning.