Principal component analysis: Difference between revisions
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Created page with "wikipedia:Principal component analysis has applications in many fields such as population genetics, microbiome studies, and atmospheric science. It is a methodology to take a wide range of data and reduce it to be able to describe or visualize the data more easily without losing too much accuracy. This video explains it {{Video|url=https://www.youtube.com/watch?v=FD4DeN81ODY}} Category:Database Category:Analysis Category:Math" |
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[[wikipedia:Principal component analysis]] has applications in many fields such as population genetics, microbiome studies, and atmospheric science. It is a methodology to take a wide range of data and reduce it to be able to describe or visualize the data more easily without losing too much accuracy. | [[wikipedia:Principal component analysis]] (PCA) has applications in many fields such as population genetics, microbiome studies, and atmospheric science. It is a methodology to take a wide range of data and reduce it to be able to describe or visualize the data more easily without losing too much accuracy. | ||
This video explains it | This video explains it | ||
Revision as of 17:03, 23 January 2025
wikipedia:Principal component analysis (PCA) has applications in many fields such as population genetics, microbiome studies, and atmospheric science. It is a methodology to take a wide range of data and reduce it to be able to describe or visualize the data more easily without losing too much accuracy.
This video explains it
