# Feature Interactions in XGBoost

arXiv: https://arxiv.org/abs/2007.05758 · HTML: https://mlsanity.com/paper/2007.05758/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [TSCAN: Context-Aware Uplift Modeling via Two-Stage Training for Online Merchant Business Diagnosis](https://mlsanity.com/paper/2504.18881/) (2025)
- [Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation](https://mlsanity.com/paper/2608.05819/) (2026)

## Abstract (arXiv)

In this paper, we investigate how feature interactions can be identified to be used as constraints in the gradient boosting tree models using XGBoost's implementation. Our results show that accurate identification of these constraints can help improve the performance of baseline XGBoost model significantly. Further, the improvement in the model structure can also lead to better interpretability.