Transfer Learning with Deep Tabular Models (2206.15306v2)

Abstract: Recent work on deep learning for tabular data demonstrates the strong performance of deep tabular models, often bridging the gap between gradient boosted decision trees and neural networks. Accuracy aside, a major advantage of neural models is that they learn reusable features and are easily fine-tuned in new domains. This property is often exploited in computer vision and natural language applications, where transfer learning is indispensable when task-specific training data is scarce. In this work, we demonstrate that upstream data gives tabular neural networks a decisive advantage over widely used GBDT models. We propose a realistic medical diagnosis benchmark for tabular transfer learning, and we present a how-to guide for using upstream data to boost performance with a variety of tabular neural network architectures. Finally, we propose a pseudo-feature method for cases where the upstream and downstream feature sets differ, a tabular-specific problem widespread in real-world applications. Our code is available at https://github.com/LevinRoman/tabular-transfer-learning .

• The paper shows that pre-trained deep tabular models outperform conventional GBDTs in low-data regimes.
• It introduces effective strategies like MLP head configurations and pseudo-feature methods to align upstream and downstream tasks.
• The study validates transfer learning using a modified MIMIC-IV dataset (MetaMIMIC) in a realistic multi-label medical diagnosis context.

Transfer Learning with Deep Tabular Models: A Critical Review

The paper presented in "Transfer Learning with Deep Tabular Models" explores the capabilities of neural network architectures in the tabular domain, heavily focusing on the concept of transfer learning. While the application of transfer learning in domains such as computer vision and natural language processing has been well-established due to the advent of powerful neural architectures, its utilization in tabular data remains underexplored. This paper addresses this gap through an extensive examination of the efficacy of representation learning in enhancing knowledge transfer between related tasks in the tabular data domain.

Key Contributions and Methodology

The paper makes several noteworthy contributions. Primarily, it delineates the advantages of deep tabular models over traditional machine learning algorithms, like Gradient Boosted Decision Trees (GBDT), particularly in contexts with limited downstream data availability. The authors provide evidence that neural networks, when equipped with transfer learning capabilities, perform superiorly compared to GBDTs, even those augmented with stacking techniques.

In the experimental setup, the researchers employ a realistic medical diagnosis scenario using a modified MIMIC-IV dataset called MetaMIMIC, which provides a robust testbed due to its multi-label classification tasks. They evaluate the transferability of features learned by deep tabular models across 12 distinct conditions while experimenting with varying amounts of available downstream labeled data.

To maximize the utility of upstream data, various models, including the FT-Transformer, TabTransformer, ResNet, and MLP, are pre-trained on an extensive dataset encompassing several related medical tasks. These models are then fine-tuned on limited data pertaining to a specific target task, simulating a common real-world challenge where ample data exists for some tasks but is scarce for others.

Observations and Results

The empirical results suggest several key findings:

• Model Performance: Across different amounts of downstream data, the FT-Transformer and MLP models with transfer learning consistently outperformed both from-scratch trained models and GBDTs, demonstrating a pronounced advantage in low-data regimes.
• Transfer Learning Setups: Analysis of various transfer learning strategies reveals that using an MLP head atop either a frozen or trainable feature extractor was particularly effective. Meanwhile, end-to-end fine-tuning with a linear head also proved beneficial for certain architectures, such as the FT-Transformer.
• Self-Supervised Learning: The interrogation of self-supervised pre-training methodologies, like Masked LLM (MLM) and contrastive learning, unveiled that supervised pre-training remains the most effective strategy for improving neural embeddings in this domain.
• Pseudo-Feature Method: The introduction of a pseudo-feature method addresses challenges of feature misalignment between upstream and downstream tasks. This method involves predicting values for missing features, which markedly boosts performance compared to disregarding those features entirely.

Implications and Future Directions

This work not only underscores the untapped potential of deep learning in the tabular domain but also steers future research towards several directions. One potential avenue is refining self-supervised learning algorithms to harness unlabelled data more effectively, akin to their successes in other fields. Furthermore, the development of more robust methods for aligning heterogeneous feature sets, possibly through advanced pseudo-feature techniques, presents a promising research frontier.

Practically, this research offers valuable insights for industries dealing with tabular data, particularly in sectors where data scarcity is common, such as healthcare and finance. The demonstrated success of transfer learning can significantly reduce the data requirements and computational costs in deploying machine learning solutions, offering a pathway to more efficient and scalable systems.

In conclusion, the paper establishes a comprehensive understanding of the capabilities and advantages of deep tabular models in transfer learning, pushing the boundaries of what is achievable in this domain and setting the stage for further advancements.