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 (and [https://builtin.com/data-science/step-step-explanation-principal-component-analysis the supporting article] by 'built-in') explains it | ||
{{Video|url=https://www.youtube.com/watch?v=FD4DeN81ODY}} | {{Video|url=https://www.youtube.com/watch?v=FD4DeN81ODY}} | ||
Latest revision as of 17:17, 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 (and the supporting article by 'built-in') explains it
