In this paper, we introduce Partial Information Decomposition of Features (PIDF), a new paradigm for simultaneous data interpretability and feature selection. Contrary to traditional methods that assign a single importance value, our approach is based on three metrics per feature: the mutual information shared with the target variable, the feature's contribution to synergistic information, and the amount of this information that is redundant. In particular, we develop a novel procedure based on these three metrics, which reveals not only how features are correlated with the target but also the additional and overlapping information provided by considering them in combination with other features. We extensively evaluate PIDF using both synthetic and real-world data, demonstrating its potential applications and effectiveness, by considering case studies from genetics and neuroscience.

本文介绍了 Partial Information Decomposition of Features（PIDF），一种同时实现数据可解释性和特征选择的新范式。相较于传统方法，我们的方法基于每个特征的三个度量值：与目标变量的相互信息、特征对协同信息的贡献以及冗余信息的数量。我们根据这三个度量值开发了一种新的程序，不仅揭示了特征与目标的相关性，还考虑了特征与其他特征的组合所提供的额外和重叠信息。我们广泛评估了PIDF，使用了合成和真实数据，并通过遗传学和神经科学领域的案例研究展示了其潜在应用和有效性。

数据解释性和特征选择的部分信息分解