Article published at Innovations in Systems and Software Engineering

We are happy to announce that our article "From diagnosis to repair: A model-driven framework for root cause analysis of machine learning pipelines" is published online in the Journal of Innovations in Systems and Software Engineering. In the article, we propose a model-driven framework for root cause analysis and hyperparameter intervention that operates solely on structured descriptors. The framework uses three dataset-complexity meta-features, namely class overlap, class imbalance, and sparsity, together with learner hyperparameters. We evaluate the approach on two model families, Decision Trees (DT) and Multilayer Perceptrons (MLP). For each family, we construct a meta-dataset comprising 81,000 pipeline runs generated from 270 datasets and 300 hyperparameter configurations.