A practical evaluation of AutoML tools for binary, multiclass, and multilabel classification

A practical evaluation of AutoML tools for binary, multiclass, and multilabel classification

Abstract Selecting the most suitable Automated Machine Learning (AutoML) tool is pivotal for achieving optimal performance in diverse classification tasks, including binary, multiclass, and multilabel scenarios. The wide range of frameworks with distinct features and capabilities complicates this de...

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Bibliographic Details
Main Authors: Marcelo V. C. Aragão, Augusto G. Afonso, Rafaela C. Ferraz, Rairon G. Ferreira, Sávio G. Leite, Felipe A. P. de Figueiredo, Samuel B. Mafra
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Subjects:
Machine learning
Classification
Hyperparameter optimization
Neural architecture search
AutoML
Online Access:https://doi.org/10.1038/s41598-025-02149-x
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