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

2024-12-31

AI Summary

Machine learning model selection lacks formalized methodologies, making it difficult to systematically characterize contextual factors—such as data characteristics and prediction tasks—and their interactions, resulting in opaque, non-adaptive decisions. This paper introduces, for the first time, software product line (SPL) principles into ML model selection, proposing a variability-aware algorithm selection framework. It constructs a configurable feature model that explicitly captures commonalities and variabilities among contextual factors—including dataset size, feature dimensionality, and task type—as well as their logical dependencies. By integrating scikit-learn’s heuristic rules with an instantiation framework, the approach enables interpretable, adaptive, and transparent model recommendations. An empirical case study demonstrates that the method significantly outperforms existing strategies in accuracy, interpretability, and contextual adaptability.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Model Selection
• Machine Learning
• Rule Formalization

Innovation

• Machine Learning Model Selection
• Automated Model Picking
• Scikit-Learn Heuristics