what is PCA in machine learning

Question

Can anyone explain PCA in Machine Learning?

Answer 1

PCA is the acronym of Principal Component Analysis. It is an important methodology in Machine Learning that is used to calculate the projections of the input data. PCA is mostly used in the field of linear algebra and statistics. The working of PCA lies in the methodology that causes a reduction in the dimensionality of the input data. The data can be projected onto a subspace with fewer sets of data compared to the original data. When working with PCA in Python, it can either be manually calculated or worked on. The methodology can be reused based on different input vectors and matrices as well. The concept of eigenvalues and eigenvectors take center stage when talking about the working of Principal Component Analysis.

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