Paper accepted at LOD 2025

We are happy to announce that our paper Empirical Evidence for Data-Centric AI: A Comparative Study of Data Complexity and Hyperparameter Effects (by Emmanuel Charleson Dapaah and Jens Grabowski) has been accepted at the The 11th International Conference on Machine Learning, Optimization, and Data Science (LOD 2025). This paper presents a comprehensive empirical study comparing the relative influence of dataset complexity and hyperparameter settings on the performance of five widely-used classification algorithms: Random Forest, Support Vector Machine, Decision Tree, Adaptive Boosting, and Multi-Layer Perceptron. The findings reveal that data-centric factors—especially class overlap (N1)—consistently exert a far stronger impact on both bias and variance than hyperparameter settings.