List of datasets for machine-learning research

This article contains dynamic lists that may never be able to satisfy particular standards for completeness. You can help by adding missing items with reliable sources.
Part of a series on
Machine learning
and data mining
Paradigms
Problems
Clustering
Dimensionality reduction
Structured prediction
Anomaly detection
Artificial neural network
Reinforcement learning
Learning with humans
Model diagnostics
Mathematical foundations
Journals and conferences
Related articles

These datasets are used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets.[1] High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce.[2][3][4]

Many organizations, including governments, publish and share their datasets. The datasets are classified, based on the licenses, as Open data and Non-Open data.

The datasets from various governmental-bodies are presented in List of open government data sites. The datasets are ported on open data portals. They are made available for searching, depositing and accessing through interfaces like Open API. The datasets are made available as various sorted types and subtypes.

  1. ^ Wissner-Gross, A. "Datasets Over Algorithms". Edge.com. Retrieved 8 January 2016.
  2. ^ Weiss, G. M.; Provost, F. (October 2003). "Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction". Journal of Artificial Intelligence Research. 19: 315–354. doi:10.1613/jair.1199.
  3. ^ Abney, Steven (2007). Semisupervised Learning for Computational Linguistics. CRC Press. ISBN 978-1-4200-1080-0.[page needed]
  4. ^ Žliobaitė, Indrė; Bifet, Albert; Pfahringer, Bernhard; Holmes, Geoff (2011). "Active Learning with Evolving Streaming Data". Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science. Vol. 6913. pp. 597–612. doi:10.1007/978-3-642-23808-6_39. ISBN 978-3-642-23807-9.