This article proposes a test procedure that can be used to test ML models and ML-based systems independently of the actual training process. In this way, the typical quality statements such as accuracy and precision of these models and system can be verified independently, taking into account their black box character and the immanent stochastic properties of ML models and their training data. The article presents first results from a set of test experiments and suggest extensions to existing test methods reflecting the stochastic nature of ML models and ML-based systems.

翻译：本文提出了一种测试流程，可用于独立于实际训练过程对机器学习模型及基于机器学习的系统进行测试。通过这种方式，这些模型与系统的典型质量声明（如准确率和精确度）可得到独立验证，同时兼顾其黑盒特性以及机器学习模型及其训练数据固有的随机属性。本文展示了基于一组测试实验的初步结果，并提出了对现有测试方法的扩展建议，以反映机器学习模型及基于机器学习的系统的随机性本质。