The Evolution of AutoML: Automated Machine Learning Techniques for Model Selection and Hyperparameter Tuning
Keywords:
AutoML, Model Selection, Hyperparameter Tuning, Machine LearningAbstract
Automated Machine Learning (AutoML) has emerged as a significant breakthrough in machine learning, simplifying the process of model selection and hyperparameter tuning, which traditionally requires deep expertise and extensive trial and error. This paper provides a comprehensive overview of the evolution of AutoML techniques, focusing on model selection and hyperparameter tuning methods. By automating these tasks, AutoML facilitates faster and more efficient machine learning development, driving broader adoption across industries. The paper highlights key algorithms, evaluates their performance, and discusses the challenges and future directions in AutoML research.
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Copyright (c) 2024 Yousif Ibrahim (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
