OpenML

OpenML is an open, collaborative platform for machine learning research and education. It provides a centralized repository for datasets, algorithms, and experimental results, aiming to make machine learning experiments more transparent and reproducible.

Core concepts on the platform include datasets (with metadata and licenses), tasks (which specify the dataset, evaluation protocol, and target variable), flows (implementations of learning algorithms or pipelines), and runs (records of executing a flow on a task, along with performance metrics). Users can organize related experiments into studies to share and compare results.

OpenML offers a REST API and client libraries for several programming languages, enabling programmatic access to data and experiments. Integrations with popular tools such as Python's scikit-learn allow users to fetch datasets, upload flows, and run experiments directly from their code, facilitating reproducible research and benchmarking.

The platform is community-driven and hosts data under various licenses. It is used in research, education, and benchmarking to compare algorithms across standardized tasks. OpenML supports reproducibility by tracking provenance, versions, and results, and it encourages collaboration across researchers and disciplines.