Principal Component Analysis

Definition of Principal Component Analysis

Principal component analysis (PCA) is a technique used to reduce the dimensionality of data. It does this by identifying the principal components of the data, which are the dimensions that account for the most variation in the data. PCA can be used to improve the performance of machine learning algorithms, and to make it easier to visualize and understand high-dimensional data.

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