Journal article

An Instance Space Analysis of Regression Problems

Mario Andres Munoz, Tao Yan, Matheus R Leal, Kate Smith-Miles, Ana Carolina Lorena, Gisele L Pappa, Romulo Madureira Rodrigues

ACM Transactions on Knowledge Discovery from Data | ASSOC COMPUTING MACHINERY | Published : 2021


The quest for greater insights into algorithm strengths and weaknesses, as revealed when studying algorithm performance on large collections of test problems, is supported by interactive visual analytics tools. A recent advance is Instance Space Analysis, which presents a visualization of the space occupied by the test datasets, and the performance of algorithms across the instance space. The strengths and weaknesses of algorithms can be visually assessed, and the adequacy of the test datasets can be scrutinized through visual analytics. This article presents the first Instance Space Analysis of regression problems in Machine Learning, considering the performance of 14 popular algorithms on ..

View full abstract


Awarded by Fundacao de Amparo a Pesquisa do Estado de Sao Paulo

Funding Acknowledgements

Funding was provided by the Australian Research Council, the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico, and the Fundacao de Amparo a Pesquisa do Estado de Sao Paulo, under grants FL140100012, 305291/2017-3, 2012/22608-8 and 2019/20328-7.