Variance measures the variation of a single random variable (like the height of a person in a population), whereas covariance is a measure of how much two random variables vary together (like the height of a person and the weight of a person in a population). Introductionīefore we get started, we shall take a quick look at the difference between covariance and variance. We will describe the geometric relationship of the covariance matrix with the use of linear transformations and eigendecomposition. This article is showing a geometric and intuitive explanation of the covariance matrix and the way it describes the shape of a data set.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |