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Principal Component Analysis - Why not to use PCA in machine learning?
Answer
Using principal component analysis (PCA) in machine learning isn't always a good idea because it applies a linear adjustment that can make the data less useful. This change makes the data less meaningful at its core, which makes it harder for the model to learn. Therefore, keep in mind that PCA has the ability to impede the process of data extraction by models.
