Benchmark and Survey of Automated Machine Learning Frameworks

Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabli...

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Bibliographic Details
Published inThe Journal of artificial intelligence research Vol. 70; pp. 409 - 472
Main Authors Zöller, Marc-André, Huber, Marco F.
Format Journal Article
LanguageEnglish
Published San Francisco AI Access Foundation 01.01.2021
Subjects
Artificial intelligence
Automation
Benchmarks
Datasets
Domains
Machine learning
Scientists
Subject specialists
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Summary:Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to build machine learning applications automatically without extensive knowledge of statistics and machine learning. This paper is a combination of a survey on current AutoML methods and a benchmark of popular AutoML frameworks on real data sets. Driven by the selected frameworks for evaluation, we summarize and review important AutoML techniques and methods concerning every step in building an ML pipeline. The selected AutoML frameworks are evaluated on 137 data sets from established AutoML benchmark suites.
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ISSN:1076-9757
1076-9757
1943-5037
DOI:10.1613/jair.1.11854