Even if a model is not globally sparse, it is possible for decisions made from that model to be accurately and faithfully described by a small number of features. For instance, an application for a large loan might be denied to someone because they have no credit history, which overwhelms any evidence towards their creditworthiness. In this work, we introduce the Sparse Explanation Value (SEV), a new way of measuring sparsity in machine learning models. In the loan denial example above, the SEV is 1 because only one factor is needed to explain why the loan was denied. SEV is a measure of decision sparsity rather than overall model sparsity, and we are able to show that many machine learning models -- even if they are not sparse -- actually have low decision sparsity, as measured by SEV. SEV is defined using movements over a hypercube, allowing SEV to be defined consistently over various model classes, with movement restrictions reflecting real-world constraints. We proposed the algorithms that reduce SEV without sacrificing accuracy, providing sparse and completely faithful explanations, even without globally sparse models.

翻译：即使模型并非全局稀疏，其决策仍可能通过少量特征得到准确且忠实的描述。例如，一笔大额贷款申请可能因申请人无信用记录而被拒绝，这一因素完全压倒了反映其信用度的其他证据。本文提出稀疏解释值（Sparse Explanation Value, SEV），这是一种衡量机器学习模型稀疏性的新方法。在上述贷款拒绝案例中，SEV为1，因为仅需一个因素即可解释贷款被拒原因。SEV衡量的是决策稀疏性而非整体模型稀疏性，我们证明众多机器学习模型（即使非稀疏模型）的实际决策稀疏性较低——以SEV度量结果为准。SEV基于超立方体上的移动路径定义，可跨不同模型类别保持定义一致性，移动约束则反映现实世界限制。我们提出的算法能在不牺牲准确性的前提下降低SEV，即使没有全局稀疏模型，也能提供稀疏且完全忠实的解释。