Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study

The emergence of machine learning (ML) has led to a transformative shift in software techniques and guidelines for building software applications that support data analysis process activities such as data ingestion, modeling, and deployment. Specifically, this shift is impacting ML model selection, which is one of the key phases in this process. Model selection is the process of selecting a model or a set of models for the analysis. There have been several advances in model selection from the standpoint of core ML methods, including basic probability measures and resampling methods. However, from a software engineering perspective, this selection is still an ad hoc and informal process. It is not supported by a design approach and representation formalism that captures the selection process and cannot support the specification of existing model selection procedures (e.g., heuristics). Further, it is not interpretable in the sense of explaining why a model has been selected and does not take into account the contextual factors and their interdependencies in the experimental evaluation that leads to a specific technique selection. In general, although the current literature provides a wide variety of ML techniques and algorithms, there is a lack of design approaches to support algorithm selection. In this paper, we present a variability-aware ML algorithm selection approach that takes into account the commonalities and variations in the model selection process. The selection adapts to the variety of contextual factors that affect the model selection, such as data characteristics, number of features, prediction type, and their intricate dependencies. The applicability of the approach is illustrated by an experimental case study based on the Scikit-Learn heuristics, in which existing model selections presented in the literature are compared with selections suggested by the approach. The proposed approach can be seen as a step towards the provision of a more explicit, adaptive, transparent, interpretable, and automated basis for model selection.