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BioData Min. 2017 Dec 11;10:36. doi: 10.1186/s13040-017-0154-4. eCollection 2017.

PMLB: a large benchmark suite for machine learning evaluation and comparison.

Author information

1
Institute for Biomedical Informatics, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, 19104 PA USA.
2
Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Kraków, Poland.

Abstract

Background:

The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists.

Results:

The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. From this study, we find that existing benchmarks lack the diversity to properly benchmark machine learning algorithms, and there are several gaps in benchmarking problems that still need to be considered.

Conclusions:

This work represents another important step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.

KEYWORDS:

Benchmarking; Data repository; Machine learning; Model evaluation

Conflict of interest statement

Not applicable. All data used in this study was publicly available online and does not contain private information about any particular individual.Not applicableThe authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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