MonoNet：通过学习单调特征实现可解释模型
MonoNet: Towards Interpretable Models by Learning Monotonic Features
摘要：
能够解释或解释机器学习模型做出的预测是至关重要的。当人们有兴趣部署数据驱动模型来做出高风险决策时，尤其如此，例如在医疗保健领域。虽然近年来人们对可解释性机器学习研究的兴趣越来越大，但该领域目前缺乏对可解释性的一致定义，一些研究人员呼吁更积极地讨论可解释性的严格方法。加入这个对话，我们在本文中声称，解释一个复杂的模型的困难源于现有的功能之间的相互作用。我们认为，通过强制执行特征和输出之间的单调性，我们能够独立于其他特征来推断单个特征对输出的影响，从而更好地理解模型。我们展示了如何通过添加新的简单层在深度学习模型中结构性地引入这种约束。我们在基准数据集上验证了我们的模型，并将我们的结果与以前提出的可解释模型进行了比较。
英文摘要：
Being able to interpret, or explain, the predictions made by a machine learning model is of fundamental importance. This is especially true when there is interest in deploying data-driven models to make high-stakes decisions, e.g. in healthcare. While recent years have seen an increasing interest in interpretable machine learning research, this field is currently lacking an agreed-upon definition of interpretability, and some researchers have called for a more active conversation towards a rigorous approach to interpretability. Joining this conversation, we claim in this paper that the difficulty of interpreting a complex model stems from the existing interactions among features. We argue that by enforcing monotonicity between features and outputs, we are able to reason about the effect of a single feature on an output independently from other features, and consequently better understand the model. We show how to structurally introduce this constraint in deep learning models by adding new simple layers. We validate our model on benchmark datasets, and compare our results with previously proposed interpretable models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
