Performance Comparison of Microsoft’s AutoML API

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

Abstract

In recent years, many software libraries for automated machine learning (AutoML), such as H2O AutoML or Auto-Sklearn, have become increasingly popular as they propose to significantly simplify the ML workflow and to sustainably reduce the time required for manual feature engineering, hyperparameter tuning as well as model selection and evaluation. Among the younger and therefore less well-known representatives is Microsoft’s ML.NET, about whose model builder API only little literature and performance comparisons exists. This paper summarizes the functionality of such frameworks and discusses general requirements for automated machine learning in more detail. Finally, several experiments compare ML.NET with already established AutoML libraries based on some datasets from the field of supervised learning with respect to model quality, the API’s scope of functions and required computational resources.

Translated title of the contributionPerformancevergleich von Microsofts AutoML API
Original languageEnglish
Title of host publication35th European Modeling and Simulation Symposium, EMSS 2023
EditorsMichael Affenzeller, Agostino G. Bruzzone, Emilio Jimenez, Francesco Longo, Antonella Petrillo
PublisherDIME UNIVERSITY OF GENOA
ISBN (Electronic)9788885741881
DOIs
Publication statusPublished - 2023
Event35th European Modeling and Simulation Symposium - Divani Caravel Hotel, Athen, Greece
Duration: 18 Sept 202320 Sept 2023
https://www.msc-les.org/emss2023/

Publication series

NameEuropean Modeling and Simulation Symposium, EMSS
Volume2023-September
ISSN (Print)2305-2023

Conference

Conference35th European Modeling and Simulation Symposium
Abbreviated titleEMSS 2023
Country/TerritoryGreece
CityAthen
Period18.09.202320.09.2023
Internet address

Keywords

  • Automated Machine Learning
  • Microsoft AutoML
  • Performance Comparison
  • Hyperparameter Tuning

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