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AMLTK: A Modular AutoML Toolkit in Python

Year: 2024
Type: article
Abstract: Machine Learning is a core building block in novel data-driven applications.Practitioners face many ambiguous design decisions while developing practical machine learning (ML) solutions.Automated machine learning (AutoML) facilitates the development of machine learning applications by providing efficient methods for optimizing hyperparameters, searching for neural architectures, or constructing whole ML pipelines (Hutter et al., 2019).Thereby, design decisions such as the choice of modelling, pre-processing, and training algorithm are crucial to obtaining well-performing solutions.By automatically obtaining ML solutions, AutoML aims to lower the barrier to leveraging machine learning and reduce the time needed to develop or adapt ML solutions for new domains or data.
DOI: 10.21105/joss.06367
Authors: Edward Bergman, Matthias Feurer, Aron Bahram, Amir Rezaei Balef, Lennart Purucker, Sarah Segel, Marius Lindauer, Frank Hutter, Katharina Eggensperger
Institutions: University of Freiburg, Munich Center for Machine Learning, Ludwig-Maximilians-Universität München, University of Tübingen, Leibniz University Hannover, L3S Research Center
Language: English
Open Access status: diamond
Awards: G1562512020, G3547540627, G434337789, G4855614693, Interactive and Explainable Human-Centered AutoML, EXC 2064: Maschinelles Lernen: Neue Perspektiven für die Wissenschaft, Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization, G8256977652, SFB 1597: Small Data
OpenAlex ID: W4401580935
FWCI: 0.295
Cites: 19
Cited by: 2
Related to: 10