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- W4313464569 abstract "AbstractAutomated Machine Learning (AutoML) is an area of research that focuses on developing methods to generate machine learning models automatically. The idea of being able to build machine learning models with very little human intervention represents a great opportunity for the practice of applied machine learning. However, there is very little information on how to design an AutoML system in practice. Most of the research focuses on the problems facing optimization algorithms and leaves out the details of how that would be done in practice. In this paper, we propose a frame of reference for building general AutoML systems. Through a narrative review of the main approaches in the area, our main idea is to distill the fundamental concepts in order to support them in a single design. Finally, we discuss some open problems related to the application of AutoML for future research.KeywordsMachine learningAutomated machine learningAutoMLApplied machine learningApplied AutoML" @default.
- W4313464569 created "2023-01-06" @default.
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- W4313464569 date "2022-01-01" @default.
- W4313464569 modified "2023-09-27" @default.
- W4313464569 title "A General Recipe for Automated Machine Learning in Practice" @default.
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- W4313464569 doi "https://doi.org/10.1007/978-3-031-22419-5_21" @default.
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