OpenML

2014/06/16 by Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl +2 · 17 citations
Computer Science · #Data Analysis with R #Data Visualization and Analytics #Time Series Analysis and Forecasting #cs.CY #cs.LG

paper · pdf · doi:10.1145/2641190.2641198

published as SIGKDD Explor. Newsl. 15, 2 (June 2014), 49-60 · 12 pages, 10 figures

openalex publication_date 2014/06/16 · arxiv created 2014/08/01 · arxiv updated 2014/08/04 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05

Abstract

Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals. In this paper, we introduce OpenML, a place for machine learning researchers to share and organize data in fine detail, so that they can work more effectively, be more visible, and collaborate with others to tackle harder problems. We discuss how OpenML relates to other examples of networked science and what benefits it brings for machine learning research, individual scientists, as well as students and practitioners.

Citations

• Classifier Technology and the Illusion of Progress
• An anatomically comprehensive atlas of the adult human brain transcriptome
• Galaxy Zoo: ‘Hanny's Voorwerp’, a quasar light echo?
• The SDSS SkyServer, Public Access to the Sloan Digital Sky Server Data

Cited by

• ScoringBench: A Benchmark for Evaluating Tabular Foundation Models with Proper Scoring Rules
• Efficient Mean Curvature Computation on High-Dimensional Data Manifolds
• Revisiting Chebyshev Polynomial and Anisotropic RBF Models for Tabular Regression
• PMLB: a large benchmark suite for machine learning evaluation and comparison
• EvalAI: Towards Better Evaluation Systems for AI Agents
• Empirical Likelihood for Contextual Bandits
• EdgeLM: Edge Demonstrations for Language Models' Table Understanding
• Coalitional Strategies for Efficient Individual Prediction Explanation
• An Open Source AutoML Benchmark
• Hyperparameters and tuning strategies for random forest
• Out-Of-The-Loop Multi-Fidelity Bayesian Optimization
• Shift Happens: Adjusting Classifiers
• Naive Automated Machine Learning -- A Late Baseline for AutoML
• Automating Data Science: Prospects and Challenges
• Automated data processing and feature engineering for deep learning and big data applications: A survey
• Imputation Strategies for Clustering Mixed-Type Data with Missing Values
• List of datasets for machine-learning research [wikipedia]

Related

• Open science in machine learning
• OpenML-Python: an extensible Python API for OpenML
• NSML: Meet the MLaaS platform with a real-world case study
• The Open Science of Deep Learning: Three Case Studies
• LinkML: An Open Data Modeling Framework