Owing to tremendous performance improvements in data-intensive domains, machine learning (ML) has garnered immense interest in the research community. However, these ML models turn out to be black boxes, which are tough to interpret, resulting in a direct decrease in productivity. Local Interpretable Model-Agnostic Explanations (LIME) is a popular technique for explaining the prediction of a single instance. Although LIME is simple and versatile, it suffers from instability in the generated explanations. In this paper, we propose a Gaussian Process (GP) based variation of locally interpretable models. We employ a smart sampling strategy based on the acquisition functions in Bayesian optimization. Further, we employ the automatic relevance determination based covariance function in GP, with separate length-scale parameters for each feature, where the reciprocal of lengthscale parameters serve as feature explanations. We illustrate the performance of the proposed technique on two real-world datasets, and demonstrate the superior stability of the proposed technique. Furthermore, we demonstrate that the proposed technique is able to generate faithful explanations using much fewer samples as compared to LIME.
翻译:由于数据密集型领域业绩的极大改善,机器学习(ML)在研究界引起了极大的兴趣,然而,这些ML模型却成了黑盒,难以解释,导致生产力直接下降。当地可解释模型-不可知解释解释(LIME)是解释单一实例的一种流行技术。虽然LIME简单,多功能,但生成的解释不稳定。在本文件中,我们提议基于当地可解释模型变异的高斯进程(GP),我们采用基于巴耶斯优化中获取功能的智能取样战略。此外,我们采用基于GP的自动关联性确定功能,每个特性都有不同的长度参数,其中长度参数的对等作为特征解释。我们用两个真实世界数据集来说明拟议技术的性能,并展示拟议技术的高度稳定性。此外,我们证明,拟议的技术能够比LIME少得多的样本产生准确性解释。