ArticleBioData mining2017
PMLB: a large benchmark suite for machine learning evaluation and comparison.
Article in BioData mining, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
54 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.Inflammatory bowel diseases · 2022Pooled it
- Beyond predictive accuracy: A case for mechanism-informed, uncertainty-aware machine learning in food microbiology.iScience · 2026Review
- Introducing openESM: A database of openly available experience sampling datasets.Behavior research methods · 2026Article
- Minimizing Stochastic Complexity with Ridge Regression.Entropy (Basel, Switzerland) · 2026Article
- SymRefine: A symbolic regression approach for refining and compressing neural networks.Neurocomputing · 2026Article
- Super greedy trees.Artificial intelligence review · 2026Article
- From Latent Manifolds to Targeted Molecular Probes: An Interpretable, Kinome-Scale Generative Machine Learning Framework for Family-Based Kinase Ligand Design.Biomolecules · 2026Article
- General OOD detection via model-aware and subspace-aware variable priority.Knowledge and information systems · 2026Article
- SRBench++ : principled benchmarking of symbolic regression with domain-expert interpretation.IEEE transactions on evolutionary computation : a publication of the IEEE Neural Networks Council · 2025Article
- OpenML: Insights from 10 years and more than a thousand papers.Patterns (New York, N.Y.) · 2025Article
- OpenClustered: an R package with a benchmark suite of clustered datasets for methodological evaluation and comparison.BMC medical research methodology · 2025Article
- Can a Transparent Machine Learning Algorithm Predict Better than Its Black Box Counterparts? A Benchmarking Study Using 110 Data Sets.Entropy (Basel, Switzerland) · 2024Article
- Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography.Bioengineering (Basel, Switzerland) · 2023Review
- Comparative performances of machine learning algorithms in radiomics and impacting factors.Scientific reports · 2023Article
- A flexible symbolic regression method for constructing interpretable clinical prediction models.NPJ digital medicine · 2023Article
- MLpronto: A tool for democratizing machine learning.PloS one · 2023Article
- Interpretable decision trees through MaxSAT.Artificial intelligence review · 2022Article
- Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers.Science advances · 2022Article
- Comparison of the Usability of Apple M1 Processors for Various Machine Learning Tasks.Sensors (Basel, Switzerland) · 2022Article
- A Virtual Sensing Concept for Nitrogen and Phosphorus Monitoring Using Machine Learning Techniques.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundThe selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists.
resultsThe present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. From this study, we find that existing benchmarks lack the diversity to properly benchmark machine learning algorithms, and there are several gaps in benchmarking problems that still need to be considered.
conclusionsThis work represents another important step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.
Indexed as
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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.