Integrating SAINT with Tree-Based Models: A Case Study in Employee Attrition Prediction

arXiv cs.LG / 4/14/2026

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Key Points

The paper addresses the difficulty of accurate employee attrition prediction from tabular HR data, where complex feature interactions are hard for standard ML pipelines to model.
It tests SAINT (a self-attention/intersample-attention transformer) as both a standalone classifier and as an embedding generator combined with tree-based models like XGBoost and LightGBM.
Experiments comparing standalone SAINT, standalone tree-based baselines, and hybrid SAINT+tree approaches find that tree-based models outperform SAINT and all hybrid variants on accuracy and generalization.
The study reports that the expected benefits of dense SAINT embeddings do not translate to improved performance with tree-based learners, potentially because tree models cannot effectively exploit high-dimensional dense representations.
The hybrid approach also reduces interpretability relative to pure tree models, leading the authors to recommend exploring other deep-learning-to-structured-data fusion strategies in future work.

Abstract

Employee attrition presents a major challenge for organizations, increasing costs and reducing productivity. Predicting attrition accurately enables proactive retention strategies, but existing machine learning models often struggle to capture complex feature interactions in tabular HR datasets. While tree-based models such as XGBoost and LightGBM perform well on structured data, traditional encoding techniques like one-hot encoding can introduce sparsity and fail to preserve semantic relationships between categorical features. This study explores a hybrid approach by integrating SAINT (Self-Attention and Intersample Attention Transformer)-generated embeddings with tree-based models to enhance employee attrition prediction. SAINT leverages self-attention mechanisms to model intricate feature interactions. In this study, we explore SAINT both as a standalone classifier and as a feature extractor for tree-based models. We evaluate the performance, generalizability, and interpretability of standalone models (SAINT, XGBoost, LightGBM) and hybrid models that combine SAINT embeddings with tree-based classifiers. Experimental results show that standalone tree-based models outperform both the standalone SAINT model and the hybrid approaches in predictive accuracy and generalization. Contrary to expectations, the hybrid models did not improve performance. One possible explanation is that tree-based models struggle to utilize dense, high-dimensional embeddings effectively. Additionally, the hybrid approach significantly reduced interpretability, making model decisions harder to explain. These findings suggest that transformer-based embeddings, while capturing feature relationships, do not necessarily enhance tree-based classifiers. Future research should explore alternative fusion strategies for integrating deep learning with structured data.