Domain experts from all fields are called upon, working with data scientists, to explore the use of ML techniques to solve their problems. Starting from a domain problem/question, ML-based problem-solving typically involves three steps: (1) formulating the business problem (problem domain) as a data analysis problem (solution domain), (2) sketching a high-level ML-based solution pattern, given the domain requirements and the properties of the available data, and (3) designing and refining the different components of the solution pattern. There has to be a substantial body of ML problem solving knowledge that ML researchers agree on, and that ML practitioners routinely apply to solve the most common problems. Our work deals with capturing this body of knowledge, and embodying it in a ML problem solving workbench to helps domain specialists who are not ML experts to explore the ML solution space. This paper focuses on: 1) the representation of domain problems, ML problems, and the main ML solution artefacts, and 2) a heuristic matching function that helps identify the ML algorithm family that is most appropriate for the domain problem at hand, given the domain (expert) requirements, and the characteristics of the training data. We review related work and outline our strategy for validating the workbench

通过与数据科学家合作，领域专家从各个领域共同探索使用机器学习技术解决问题。本文侧重于领域问题、机器学习问题和主要机器学习解决方案的表示，以及帮助领域专家探索机器学习解决空间的启发式匹配函数，以确定适合领域问题的机器学习算法族。

问题匹配与解决：解决机器学习问题的可解释方法