Towards the Realization of a DSML for Machine Learning: A Baseball Analytics Use Case

Abstract

Using machine learning (ML) for big data is challenging, requiring specialized knowledge of the domain, learning algorithms, and software engineering. To demonstrate the viability of model-driven engineering in the ML domain we consider an ML use case of baseball analytics by extending and applying an existing, but untested, ML domain specific modeling language (DSML). Additionally, we aim to make ML software development more accessible and formalized, and help facilitate future research in this area. This paper describes our plan, initial work, and anticipated contributions in extending, testing, and validating this DSML, and implementing a code generation scheme that is targeted at a binary classification baseball problem.

Keywords: Model driven engineering * Domain specific modeling languages * Machine Learning * Analytics * Baseball