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5.2 Principal Component Analysis (PCA) and Dimensionality Reduction

In machine learning, datasets often encompass a multitude of features, resulting in high-dimensional data spaces. These high-dimensional datasets present several challenges: they can be arduous to visualize effectively, computationally demanding to process, and may potentially lead to a deterioration in model performance.

This latter phenomenon is commonly referred to as the curse of dimensionality, a term that encapsulates the various difficulties that arise when working with data in high-dimensional spaces. To address these challenges, data scientists and machine learning practitioners employ dimensionality reduction techniques. These methods are designed to mitigate the aforementioned ...