PCA in machine learning is based on some mathematical concepts, which include Variance and covariance, Eigenvalues, and eigen factors. The principal components would be equal to the number of original variables in the given dataset. PCA involves the transformation of variables in the dataset into a new set of variables which are called PCs (Principal Components). PCA works on some assumptions which are to be followed and it helps developers maintain a standard. PCA helps in identifying relationships among different variables & then coupling them. ML models with many input variables or higher dimensionality tend to fail when operating on a higher input dataset. PCA is an unsupervised statistical technique that is used to reduce the dimensions of the dataset. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career. Enroll for the Machine Learning Course from the World’s top Universities.
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