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Is my benchmark of datasets challenging enough?

Grant number: 22/10683-7
Support Opportunities:Scholarships in Brazil - Post-Doctorate
Effective date (Start): September 01, 2022
Effective date (End): August 31, 2023
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:Ana Carolina Lorena
Grantee:João Luiz Junho Pereira
Host Institution: Divisão de Ciência da Computação (IEC). Instituto Tecnológico de Aeronáutica (ITA). Ministério da Defesa (Brasil). São José dos Campos , SP, Brazil
Associated research grant:21/06870-3 - Beyond algorithm selection: meta-learning for data and algorithm analysis and understanding, AP.JP2


Whenever a new supervised Machine Learning (ML) algorithm or solu- tion is developed, it is imperative to evaluate the predictive performance it attains for diverse datasets. This is done in order to stress out the strengths and weak- nesses of the algorithms and evidence for which situations they are most useful. A common practice is to gather some datasets from public benchmark repositories for such an evaluation. But little or no specific criteria are used in the selection of these datasets, which is often ad-hoc. In this project we will investigate the importance of properly building a diverse and challenging benchmark of datasets in order to properly evaluate ML models and really understand their capabilities. (AU)

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