Measuring the complexity of regression problems
Aron I. Maciel, Ivan G. Costa, Ana Carolina Lorena · 2016
Many works have attempted to characterize the complexity of classification problems by measures extracted from their learning datasets. These indexes provide indicatives of the inherent difficulty in solving a given classification problem. Although regression problems are equally frequent, there is a lack of studies in Machine Learning dedicated to understanding their complexity. This paper proposes some measures aimed to characterize the complexity of regression problems. They are experimentally evaluated on a set of synthetic datasets with different complexities. The results show that various measures and their combinations are able to distinguish simple linear problems from more complex variants.