Variability-Aware Machine Learning Model Selection
An approach that treats the choice of ML algorithm as a configurable process rather than an ad hoc decision, by modeling the commonalities and variation points of model selection - contextual factors such as data characteristics, number of features, prediction type, and their interdependencies - as a software-product-line feature model. Students learn how making these variation points explicit yields a more transparent, interpretable, and automatable basis for selecting a model, illustrated by instantiating the Scikit-Learn estimator-selection heuristics.
Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and
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…