面向CRISP-ML（Q）：一个具有软件测试方法的机器学习过程模型（CS LG）---Elva

我们提出了一个应用于开发机器学习应用程序的过程模型，它为来自工业界和学术界的机器学习专业人士和项目组织提供了一份任务清单，这份任务清单涵盖了从最初的想法雏形到机器学习应用程序的持续维护的这整个项目生命周期。在每项任务中，我们提出的软件测试方法都是从实践经验和科学文献中汲取的，并且已经证明是普适且稳定的，足以将其纳入最佳实践。我们对CRISP-DM进行了拓展，CRISP-DM是一个数据挖掘过程模型，已得到了强大的行业支持，但仍缺乏解决特定机器学习任务的能力。

原文题目：Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

原文：We propose a process model for the development of machine learning applications. It guides machine learning practitioners and project organizations from industry and academia with a checklist of tasks that spans the complete project life-cycle, ranging from the very first idea to the continuous maintenance of any machine learning application. With each task, we propose quality assurance methodology that is drawn from practical experience and scientific literature and that has proven to be general and stable enough to include them in best practices. We expand on CRISP-DM, a data mining process model that enjoys strong industry support but lacks to address machine learning specific tasks.

原文作者：Stefan Studer, Thanh Binh Bui, Christian Drescher, Alexander Hanuschkin, Ludwig Winkler, Steven Peters, Klaus-Robert Mueller

原文链接：https://arxiv.org/abs/2003.05155