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We have evaluated Automated Machine Learning tools on their capabilities in the common machine learning pipeline

Towards Automated Machine Learning: Evaluation And Comparison Of Automl Approaches And Tools

2019 IEEE 31ST INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2019), pp.1471-1479, (2019)

Cited by: 10|Views101
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Abstract

There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years. ML engineers, as a consequence, are in high demand across the industry, yet improving the efficiency of ML engineers remains a fundamental challenge. Automated machine learning (AutoML) has emerged as a way to save time and...More

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Funding
  • Investigates the current state of AutoML tools aiming to automate these tasks
  • The goal of this paper is to address the following questions: what are the available ML functionalities provided by the tools; how the tools perform when facing a wide spectrum of real world datasets; find the trade-off between optimization speed and accuracy of the results; and the reproducibility of the results
  • Evaluates the performance of a selected subset of these tools on a large variety of datasets and a range of supervised ML tasks
  • Evaluates a selected subset 1 of AutoML tools on nearly 300 datasets collected from Openml , which allows users to query data for different use cases
Author
Anh Truong
Anh Truong
Austin Walters
Austin Walters
Jeremy Goodsitt
Jeremy Goodsitt
Keegan E. Hines
Keegan E. Hines
C. Bayan Bruss
C. Bayan Bruss
Reza Farivar
Reza Farivar
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