Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes (2502.02672v2)

Abstract: LLMs perform remarkably well on tabular datasets in zero- and few-shot settings, since they can extract meaning from natural language column headers that describe features and labels. Similarly, TabPFN, a recent non-LLM transformer pretrained on numerous tables for in-context learning, has demonstrated excellent performance for dataset sizes up to a thousand samples. In contrast, gradient-boosted decision trees (GBDTs) are typically trained from scratch on each dataset without benefiting from pretraining data and must learn the relationships between columns from their entries alone since they lack natural language understanding. LLMs and TabPFN excel on small tabular datasets where a strong prior is essential, yet they are not competitive with GBDTs on medium or large datasets, since their context lengths are limited. In this paper, we propose a simple and lightweight approach for fusing LLMs and TabPFN with gradient-boosted decision trees, which allows scalable GBDTs to benefit from the natural language capabilities and pretraining of transformers. We name our fusion methods LLM-Boost and PFN-Boost, respectively. While matching or surpassing the performance of the transformer at sufficiently small dataset sizes and GBDTs at sufficiently large sizes, LLM-Boost and PFN-Boost outperform both standalone components on a wide range of dataset sizes in between. We demonstrate state-of-the-art performance against numerous baselines and ensembling algorithms. We find that PFN-Boost achieves the best average performance among all methods we test for all but very small dataset sizes. We release our code at http://github.com/MayukaJ/LLM-Boost .

• The paper introduces LLM-Boost and PFN-Boost, novel methods that fuse large language models or TabPFN with gradient-boosted decision trees to improve tabular data modeling.
• Experiments demonstrate that PFN-Boost consistently outperforms traditional GBDTs and other ensembles, particularly on medium to large datasets, by leveraging transformer pretraining.
• The research suggests practical benefits like achieving superior performance without extensive LLM fine-tuning and encourages exploring hybrid models balancing pretraining with scalability.

Analysis of "Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes"

The paper entitled "Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes" introduces a compelling approach for enhancing the performance of gradient-boosted decision trees (GBDTs) using LLMs and TabPFN, a pretrained tabular transformer. The integration, termed LLM-Boost and PFN-Boost, leverages the semantic understanding capabilities of transformers to address the limitations of traditional GBDTs, which lack natural language processing capabilities and are typically trained from scratch.

GBDTs have been the dominant method for modeling tabular data due to their efficient training and competitive performance across various dataset scales. However, they face significant challenges: they do not utilize the semantic information present in column headers, and they lack the ability to leverage previously unseen datasets' information, which LLMs manage through extensive pretraining. While LLMs and TabPFN perform excellently on smaller datasets due to their language understanding capabilities, their effectiveness diminishes on larger datasets due to context length limitations and high computational costs associated with fine-tuning.

The proposed methods, LLM-Boost and PFN-Boost, provide a seamless fusion of LLMs and TabPFN with GBDTs. LLM-Boost utilizes predictions from LLMs as baseline scores, which are then refined by learning their residuals through GBDTs. This approach employs a scaling parameter to balance influence from both model types, ensuring the decision tree component can efficiently handle residual learning without being overshadowed by the initial transformer predictions. Notably, PFN-Boost is highlighted as a superior method across medium-sized datasets, wherein GBDTs scale effectively while utilizing insights from the transformer’s pretrained understanding.

Experimental evaluations highlight that both LLM-Boost and PFN-Boost outperform traditional GBDT approaches and other ensemble methods like simple selection and stacking, across an array of dataset sizes. The comprehensive experimental framework includes rigorous hyperparameter tuning facilitated by Optuna, emphasizing computational efficiency. The results indicate that PFN-Boost consistently achieves top performance, particularly on medium to large datasets, where pretraining benefits are maximized, and computational overheads of LLMs become prohibitive.

The implications of this research are significant both practically and theoretically. Practical impacts include the possibility of using lightweight boosting mechanisms to achieve superior model performance across diverse dataset complexities without the added computational costs of extensive LLM fine-tuning. Theoretically, this paper encourages further exploration into model architectures that can balance pretrained models' benefits with traditional machine learning algorithms' scalability and efficiency.

Future research could focus on extending these combined models' applicability to datasets lacking explicit semantic column headers or exploring the integration of these fusion methods into automated machine learning frameworks for broader accessibility. Additionally, exploring more extensive use of advanced LLMs with long-context capabilities could potentially reduce the reliance on GBDTs, creating a paradigm shift in handling tabular data for large dataset regimes. The code release associated with this paper will undoubtedly enhance reproducibility and further exploration in this field.

In conclusion, the fusion of transformer capabilities with GBDTs presents a robust method for dealing with tabular datasets, optimizing performance through the innovative combination of pretraining and residual learning. This work serves as a foundation for future advancements in scalable and efficient machine learning solutions for tabular data.