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

TL;DR

This paper introduces a variability-aware approach for machine learning model selection that explicitly captures contextual factors and their dependencies, aiming to improve transparency, adaptability, and automation in the selection process.

Contribution

It proposes a formal, design-oriented method for ML model selection that considers variability and dependencies, supported by an experimental case study with Scikit-Learn heuristics.

Findings

The approach improves transparency and interpretability of model selection.

It outperforms traditional ad hoc selection methods in the case study.

The method supports automation and adaptability in ML workflows.

Abstract

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. 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, is not supported by a design approach and representation formalism that explicitly captures the selection process and can not support the specification of existing model selection procedures. The selection adapts to a variety of contextual factors that affect the model selection, such as data characteristics, number of features, prediction type, and their intricate dependencies. Further, it does not provide an explanation for selecting a model and does not consider the contextual factors and their interdependencies when selecting a technique. 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 considers the commonalities and variations in the model selection process. The approach's applicability 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 providing a more explicit, adaptive, transparent, interpretable, and automated basis for model selection.