![]() ![]() ![]() Now we are going to add an orthogonal line to the first line.Ĭomponents are a linear transformation that chooses a variable system for the dataset such that the greatest variance of the dataset comes to lie on the first axis. Here we have some data plotted with two features x and y and we had a regression line of best fit. So let us visualize what does it mean with an example. Eg-A dataset with 3 features or variable will have 3-dimensional space. The number of dimensions will be the same as there are a number of variables. Here, n-dimensional space is a variable sample space. Actually, the lines are perpendicular to each other in the n-dimensional space. Orthogonal means these lines are at a right angle to each other. So, in regression, we usually determine the line of best fit to the dataset but here in the PCA, we determine several orthogonal lines of best fit to the dataset. In simple words, suppose you have 30 features column in a data frame so it will help to reduce the number of features making a new feature which is the combined effect of all the feature of the data frame. Principal Component Analysis(PCA) is an unsupervised statistical technique used to examine the interrelation among a set of variables in order to identify the underlying structure of those variables. ![]()
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