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OpenML: [493] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms. PMLB: [494] A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms ...
This is a list of datasets for machine learning research. It is part of the list of datasets for machine-learning research. These datasets consist primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification.
Pages in category "Datasets in machine learning" The following 12 pages are in this category, out of 12 total. This list may not reflect recent changes. ...
Lists Wikipedia:WikiProject Lists Template:WikiProject Lists List This article has not yet received a rating on the project's importance scale . Text and/or other creative content from List of datasets for machine-learning research was copied or moved into List of datasets in computer vision and image processing .
Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
The Overhead Imagery Research Data Set (OIRDS) is a collection of an open-source, annotated, overhead images that computer vision researchers can use to aid in the development of algorithms. [1] Most computer vision and machine learning algorithms function by training on a large set of example data. [ 2 ]
Datasets in machine learning (1 C, 12 P) S. Statistical data sets (18 C, 32 P) Pages in category "Datasets" The following 4 pages are in this category, out of 4 total.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]