What Is Principal Component Analysis? A Simplified Explanation
Principal Component Analysis (PCA) is a statistical technique designed to reduce the dimensionality of a dataset while preserving as much of the original information as possible. It does so by identifying the principal directions in which the data varies the most.
In simple terms, consider a large dataset in the form of a table, where each column represents a different feature or variable. These features may exhibit redundancy or strong correlations. PCA reduces the number of columns by transforming them into a smaller set of uncorrelated variables called "principal components," each capturing a significant portion of the data’s variance or underlying patterns.
This process can be visualized by imagining a complex three-dimensional object being projected onto a two-dimensional surface. When illuminated from a specific angle, the resulting 2D shadow retains the most prominent shape characteristics of the 3D object while omitting some finer details. Similarly, each principal component in PCA is a lower-dimensional representation that captures the most salient features of the original data.
PCA thus serves as a method of data simplification. It identifies the most informative aspects of the dataset and expresses them using fewer variables, making the data easier to interpret and analyze.
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