Towards Automated Variability-Aware Machine-Learning-Based Modeling Analysis

Abstract

Data analysis involves the use of a wide variety of systems and libraries to support the exploration and development of models that can uncover valuable patterns and enable individuals and businesses to draw informed insights. However, efforts towards the automation of the ML-based data analysis modeling process faces numerous challenges. In this paper, we describe our ongoing work towards the automation of the data analysis modeling phase based on a variability-aware approach. This approach involves capturing the variabilities through feature models, designing an automated framework to support the analysis, and developing use cases. The work advances the state of the art in the development of methods and tools to support the automation of ML-based data analysis.