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Subsampling: Difference between revisions
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(Created page with "{{see also|Machine learning terms}} ==Definition== Subsampling, also known as '''downsampling''', is a technique used in machine learning and statistics to reduce the size of a dataset by selecting a smaller representative subset of the data. This process is applied to decrease the computational complexity and memory requirements of machine learning algorithms, while maintaining the quality of the obtained results. Subsampling is especially useful when dealing wi...") |
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Subsampling, also known as '''downsampling''', is a technique used in [[machine learning]] and [[statistics]] to reduce the size of a dataset by selecting a smaller representative subset of the data. This process is applied to decrease the computational complexity and memory requirements of machine learning algorithms, while maintaining the quality of the obtained results. Subsampling is especially useful when dealing with large-scale datasets or when computational resources are limited. | Subsampling, also known as '''downsampling''', is a technique used in [[machine learning]] and [[statistics]] to reduce the size of a dataset by selecting a smaller representative subset of the data. This process is applied to decrease the computational complexity and memory requirements of machine learning algorithms, while maintaining the quality of the obtained results. Subsampling is especially useful when dealing with large-scale datasets or when computational resources are limited. | ||
==Methods of Subsampling | ==Methods of Subsampling== | ||
There are various methods used for subsampling in machine learning, depending on the type of data and the problem being addressed. Some common methods include: | There are various methods used for subsampling in machine learning, depending on the type of data and the problem being addressed. Some common methods include: | ||