论文标题
通过半自动化的API包装来提高机器学习API的学习性
Improving the Learnability of Machine Learning APIs by Semi-Automated API Wrapping
论文作者
论文摘要
想要进入机器学习世界（ML）的学生和专业软件开发人员的主要障碍，不仅掌握了科学背景，还掌握了可用的ML API。因此，我们应对创建易于学习和使用的API的挑战，尤其是新手。但是，尚不清楚如果不损害表现力，如何实现这一目标。我们研究了广泛使用的ML API Scikit-Learn的这个问题。在本文中，我们分析了Kaggle社区的使用，确定了未使用的API的未使用的部分，而无用的部分可以消除而不会影响客户程序。此外，我们讨论了其余部分中的可用性问题，提出了相关的设计改进，并展示了如何通过对现有第三方API的半自动化包装来实施它们。
A major hurdle for students and professional software developers who want to enter the world of machine learning (ML), is mastering not just the scientific background but also the available ML APIs. Therefore, we address the challenge of creating APIs that are easy to learn and use, especially by novices. However, it is not clear how this can be achieved without compromising expressiveness. We investigate this problem for scikit-learn, a widely used ML API. In this paper, we analyze its use by the Kaggle community, identifying unused and apparently useless parts of the API that can be eliminated without affecting client programs. In addition, we discuss usability issues in the remaining parts, propose related design improvements and show how they can be implemented by semi-automated wrapping of the existing third-party API.
