mlsanityData to 14 Oct 2026

Benchmarking Automatic Machine Learning Frameworks

arXiv 1808.06492 ↗First posted 17 Aug 2018cs.LG cs.AI stat.MLcited by 15

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Abstract (arXiv)

AutoML serves as the bridge between varying levels of expertise when designing machine learning systems and expedites the data science process. A wide range of techniques is taken to address this, however there does not exist an objective comparison of these techniques. We present a benchmark of current open source AutoML solutions using open source datasets. We test auto-sklearn, TPOT, auto_ml, and H2O's AutoML solution against a compiled set of regression and classification datasets sourced from OpenML and find that auto-sklearn performs the best across classification datasets and TPOT performs the best across regression datasets.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Aug 2018.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 4; 2024: 1; 2025: 2.

15 papers.

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  1. 2022AMLB: an AutoML Benchmarkcited by 55
  2. 2021Towards Green Automated Machine Learning: Status Quo and Future Directionscited by 25
  3. 2022Task Selection for AutoML System Evaluationcited by 7
  4. 2022Automated machine learning: AI-driven decision making in business analyticscited by 5
  5. 2024The Potential of AutoML for Recommender Systemscited by 3
  6. 2021A Neophyte With AutoML: Evaluating the Promises of Automatic Machine Learning Toolscited by 2
  7. 2023Assessing the Use of AutoML for Data-Driven Software Engineeringcited by 2
  8. 2021Benchmarking AutoML Frameworks for Disease Prediction Using Medical Claimscited by 1
  9. 2023AutoML Systems For Medical Imagingcited by 1
  10. 2021Post-processing Multi-Model Medium-Term Precipitation Forecasts Using Convolutional Neural Networkscited by 0
  11. 2021Surrogate Model Based Hyperparameter Tuning for Deep Learning with SPOTcited by 0
  12. 2023Predictability of Machine Learning Algorithms and Related Feature Extraction Techniquescited by 0
3 more
  1. 2023STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centerscited by 0
  2. 2025BOASF: A Unified Framework for Speeding up Automatic Machine Learning via Adaptive Successive Filteringcited by 0
  3. 2025AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Datacited by 0