Confidence predictors can deliver predictions with the associated confidence required for decision making and can play an important role in drug discovery and toxicity predictions. In this work we investigate a recently introduced version of conformal prediction, synergy conformal prediction, focusing on the predictive performance when applied to bioactivity data. We compare the performance to other variants of conformal predictors for multiple partitioned datasets and demonstrate the utility of synergy conformal predictors for federated learning where data cannot be pooled in one location. Our results show that synergy conformal predictors based on training data randomly sampled with replacement can compete with other conformal setups, while using completely separate training sets often results in worse performance. However, in a federated setup where no method has access to all the data, synergy conformal prediction is shown to give promising results. Based on our study, we conclude that synergy conformal predictors are a valuable addition to the conformal prediction toolbox.

中文翻译：

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应用于大规模生物活性数据集和联邦学习的协同共形预测

置信度预测器可以提供决策所需的相关置信度的预测，并且可以在药物发现和毒性预测中发挥重要作用。在这项工作中，我们研究了最近引入的保形预测版本，协同保形预测，重点关注应用于生物活性数据时的预测性能。我们将性能与多个分区数据集的保形预测器的其他变体进行了比较，并展示了协同保形预测器在联邦学习中的效用，其中数据不能集中在一个位置。我们的结果表明，基于随机采样的训练数据的协同保形预测器可以与其他保形设置竞争，而使用完全独立的训练集通常会导致更差的性能。然而，在没有方法可以访问所有数据的联合设置中，协同共形预测显示出有希望的结果。根据我们的研究，我们得出结论，协同共形预测器是共形预测工具箱的一个有价值的补充。