LIME的统计稳定性指标：获得机器学习模型的可靠解释
Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning models
摘要：
如今，我们正在目睹业务流程向更计算驱动的方法转变。机器学习技术的不断增加的使用是这种趋势的最明显的例子。这种革命通常提供优势，例如提高预测准确性和缩短获得结果的时间。然而，这些方法存在一个主要缺点：很难理解算法做出决定的依据。为了解决这个问题，我们考虑LIME方法。我们给出了LIME的一般背景，然后，我们专注于稳定性问题：在相同条件下重复使用该方法，可能会产生不同的解释。两个互补的指标，提出了衡量石灰稳定性。从业者必须意识到这个问题，并有一个工具来发现它。稳定性保证LIME解释是可靠的，因此通过建议的指数进行稳定性评估是至关重要的。作为案例研究，我们将机器学习和经典统计技术应用于信用风险数据。我们在机器学习算法上测试LIME并检查其稳定性。最后，我们检查返回的解释是否合理。
英文摘要：
Nowadays we are witnessing a transformation of the business processes towards a more computation driven approach. The ever increasing usage of Machine Learning techniques is the clearest example of such trend. This sort of revolution is often providing advantages, such as an increase in prediction accuracy and a reduced time to obtain the results. However, these methods present a major drawback: it is very difficult to understand on what grounds the algorithm took the decision. To address this issue we consider the LIME method. We give a general background on LIME then, we focus on the stability issue: employing the method repeated times, under the same conditions, may yield to different explanations. Two complementary indices are proposed, to measure LIME stability. It is important for the practitioner to be aware of the issue, as well as to have a tool for spotting it. Stability guarantees LIME explanations to be reliable, therefore a stability assessment, made through the proposed indices, is crucial. As a case study, we apply both Machine Learning and classical statistical techniques to Credit Risk data. We test LIME on the Machine Learning algorithm and check its stability. Eventually, we examine the goodness of the explanations returned.
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。
