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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 ...
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.
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. [1] Such algorithms function by making data-driven predictions or decisions, [2] through building a mathematical model from input data. These input data used to build the model are usually divided into multiple data ...
The set of images in the MNIST database was created in 1994. Previously, NIST released two datasets: Special Database 1 (NIST Test Data I, or SD-1); and Special Database 3 (or SD-2). They were released on two CD-ROMs. SD-1 was the test set, and it contained digits written by high school students, 58,646 images written by 500 different writers.
Under the Obama Administration, over 1 billion dollars were spent developing databases designed for improving the educational system, including P-20 longitudinal data systems. Although these databases do contain extensive personally identifiable information , much of this information is "not kept in a format that allows officials to easily ...
High School and Beyond (HS&B) is a longitudinal study of a nationally representative sample of people who were high school sophomores and seniors in 1980. The study was originally funded by the United States Department of Education ’s National Center for Education Statistics (NCES) as a part of their Secondary Longitudinal Studies Program .
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. ...