Abstract
Meta-learning (MtL) is usually focused on analyzing a collection of datasets and how their characteristics influence Machine Learning (ML) classification performance. However, using this framework at a more fine-grained instance level is also possible, where characteristics from each observation (instance) in a dataset are related to algorithmic performance. Herewith, one can explore for …