Soft Decision Tree

Soft decision trees (SDTs) are differentiable alternatives to traditional decision trees, enabling their integration into gradient boosting machines and neural networks for improved model performance and interpretability. Current research focuses on developing SDT-based architectures for various tasks, including regression, classification, and reinforcement learning, often incorporating techniques like variational inference and attention mechanisms to enhance accuracy and uncertainty quantification. This work addresses limitations of traditional decision trees and black-box neural networks by offering models that balance predictive power with explainability, impacting fields such as fraud detection, molecular property prediction, and control systems where interpretability and reliable uncertainty estimates are crucial.