ArticleScientific reports2022
Benchmarking AutoML for regression tasks on small tabular data in materials design.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Predicting Post-Radiotherapy Epigenetic Age Acceleration From Pre-Treatment Data Using a Machine Learning Framework in Head and Neck Cancer Patients.Cancer medicine · 2026Article
- A comprehensive benchmark of active learning strategies with AutoML for small-sample regression in materials science.Scientific reports · 2025Article
- Disparities in accelerated brain aging in recent-onset and chronic schizophrenia.Psychological medicine · 2025Article
- In silico assessment of nanoparticle toxicity powered by the Enalos Cloud Platform: Integrating automated machine learning and synthetic data for enhanced nanosafety evaluation.Computational and structural biotechnology journal · 2024Article
- Application of a Machine Learning-Based Classification Approach for Developing Host Protein Diagnostic Models for Infectious Disease.Diagnostics (Basel, Switzerland) · 2024Article
- Using machine learning surrogate modeling for faster QSP VP cohort generation.CPT: pharmacometrics & systems pharmacology · 2023Article
- Accurate detection of paroxysmal atrial fibrillation with certified-GAN and neural architecture search.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Machine Learning has become more important for materials engineering in the last decade. Globally, automated machine learning (AutoML) is growing in popularity with the increasing demand for data analysis solutions. Yet, it is not frequently used for small tabular data. Comparisons and benchmarks already exist to assess the qualities of AutoML tools in general, but none of them elaborates on the surrounding conditions of materials engineers working with experimental data: small datasets with less than 1000 samples. This benchmark addresses these conditions and draws special attention to the overall competitiveness with manual data analysis. Four representative AutoML frameworks are used to evaluate twelve domain-specific datasets to provide orientation on the promises of AutoML in the field of materials engineering. Performance, robustness and usability are discussed in particular. The results lead to two main conclusions: First, AutoML is highly competitive with manual model optimization, even with little training time. Second, the data sampling for train and test data is of crucial importance for reliable results.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.