深入研究表格式LIME
Looking Deeper into Tabular LIME
摘要：
在本文中，我们对表格数据情况下LIME的默认实现进行了彻底的理论分析。我们证明，在大样本的限制，可解释的系数提供的表格石灰可以计算在一个明确的方式作为一个函数的算法参数和一些期望计算相关的黑盒模型。当要解释的函数具有一些很好的代数结构（线性，乘法或稀疏地依赖于坐标的子集）时，我们的分析为LIME提供的解释提供了有趣的见解。这些可以应用于一系列机器学习模型，包括高斯核或CART随机森林。作为一个例子，对于线性函数，我们表明，LIME具有理想的属性，提供的解释是成比例的函数的系数来解释，并忽略不使用的函数来解释坐标。对于基于分区的回归，另一方面，我们表明，LIME产生不希望的文物，可能会提供误导性的解释。
英文摘要：
In this paper, we present a thorough theoretical analysis of the default implementation of LIME in the case of tabular data. We prove that in the large sample limit, the interpretable coefficients provided by Tabular LIME can be computed in an explicit way as a function of the algorithm parameters and some expectation computations related to the black-box model. When the function to explain has some nice algebraic structure (linear, multiplicative, or sparsely depending on a subset of the coordinates), our analysis provides interesting insights into the explanations provided by LIME. These can be applied to a range of machine learning models including Gaussian kernels or CART random forests. As an example, for linear functions we show that LIME has the desirable property to provide explanations that are proportional to the coefficients of the function to explain and to ignore coordinates that are not used by the function to explain. For partition-based regressors, on the other side, we show that LIME produces undesired artifacts that may provide misleading explanations.
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