# FUTURE: Flexible Unlearning for Tree Ensemble

## TL;DR

FUTURE introduces a gradient-based, probabilistic approach to unlearning in tree ensembles, enabling efficient forgetting of sensitive data while maintaining model performance across large-scale datasets.

## Contribution

It presents a novel unlearning algorithm for tree ensembles that overcomes limitations of previous methods by using probabilistic models and gradient optimization.

## Key findings

- FUTURE achieves effective unlearning on real-world datasets.
- The method is scalable to large datasets.
- Unlearning performance is significantly improved compared to existing approaches.

## Abstract

Tree ensembles are widely recognized for their effectiveness in classification tasks, achieving state-of-the-art performance across diverse domains, including bioinformatics, finance, and medical diagnosis. With increasing emphasis on data privacy and the \textit{right to be forgotten}, several unlearning algorithms have been proposed to enable tree ensembles to forget sensitive information. However, existing methods are often tailored to a particular model or rely on the discrete tree structure, making them difficult to generalize to complex ensembles and inefficient for large-scale datasets. To address these limitations, we propose FUTURE, a novel unlearning algorithm for tree ensembles. Specifically, we formulate the problem of forgetting samples as a gradient-based optimization task. In order to accommodate non-differentiability of tree ensembles, we adopt the probabilistic model approximations within the optimization framework. This enables end-to-end unlearning in an effective and efficient manner. Extensive experiments on real-world datasets show that FUTURE yields significant and successful unlearning performance.