![]() ![]() ![]() Here is a quick visual of two-dimensional data plotted with their eigenvectors. There is a post on Stack Exchange which beautifully explains it. PCA uses Linear Algebra concepts known as Eigenvectors and Eigenvalues. PCA finds the axis with the maximum variance and projects the points onto this axis. After these features have been identified, you can use only the most important features, or those that explain the most variance, to train a machine learning model and improve the computational performance of the model without sacrificing accuracy. It uses linear algebra to determine the most important features of a dataset. ![]() Principal Component Analysis or PCA is a dimensionality reduction technique for data sets with many features or dimensions. ![]()
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