Generative and reproducible benchmarks or comprehensive evaluation machine learning classifiers

Understanding the strengths and weaknesses of machine learning (ML) algorithms is crucial to determine their scope of application. Here, we introduce the Diverse and Generative ML Benchmark (DIGEN), a collection of synthetic datasets for comprehensive, reproducible, and interpretable benchmarking of...

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Detalles Bibliográficos
Autores principales: Orzechowski, Patryk, Moore, Jason H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2022
Materias:
Social and Interdisciplinary Sciences
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9683726/
https://www.ncbi.nlm.nih.gov/pubmed/36417520
http://dx.doi.org/10.1126/sciadv.abl4747

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9683726/
https://www.ncbi.nlm.nih.gov/pubmed/36417520
http://dx.doi.org/10.1126/sciadv.abl4747

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