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Principal Component Analysis - When should PCA be applied in machine learning?


When it comes to machine learning, Principal Component Analysis (PCA) shines for two key reasons. To start with, it helps make high-dimensional data more understandable and interpretable by reducing it to two or three dimensions. Second, principal component analysis (PCA) is an excellent tool for improving the efficiency and effectiveness of subsequent machine learning methods, such as clustering and classification, during their pre-processing phases.