AUTOMATED MACHINE LEARNING: EMPOWERING DATA-DRIVEN DECISIONS WITH TPOT AND AUTO-SKLEARN    

Authors : Mohd Hyder Gouri; Mr. Anuj Kumar

Publishing Date : 2023

DOI : https://doi.org/10.52458/9789388996747.nsp2023.eb.ch-03

ISBN : 978-93-88996-92-1

Pages : 18

Chapter id : GU/NSP/EB/EFMLDSP/2023/Ch-03

Abstract : This chapter explores the fast-developing topic of automated machine learning (AutoML), which aims to democratize and streamline the machine learning process. We examine the idea of AutoML and its importance in lowering the entry barriers for machine learning so that people with different degrees of competence can use it. TPOT (Tree-Based Pipeline Optimization Tool) and Auto-sklearn, two prominent AutoML tools, are thoroughly investigated. While Auto-sklearn makes efficient use of Bayesian optimization, TPOT uses genetic programming to automate the development and optimization of machine learning pipelines. For those wishing to employ these potent tools in their machine learning projects, we also evaluate the advantages and disadvantages of AutoML while offering real-world use cases and automation advice.

Keywords : AutoML, Automated Machine Learning, TPOT, Auto-sklearn, Genetic Programming, Bayesian Optimization, Machine Learning Pipelines, Pros and Cons of AutoML, Automation Tips

Cite : Gouri, M. H., & Kumar, A. (2023). Automated Machine Learning: Empowering Data-Driven Decisions With Tpot And Auto-Sklearn (1st ed., p. 18). Noble Science Press. https://doi.org/10.52458/9789388996747.nsp2023.eb.ch-03

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