Principal Component Analysis

Definition updated on November 2023

What is Principal Component Analysis (PCA)?

The principal component analysis is an unsupervised learning technique that is used in machine learning to minimize dimensionality. Using the same transformation, it is a statistical process that transforms observations of correlated qualities into a set of linearly uncorrelated data. The Principal Components are these newly altered features. This is one of the often-used instruments for exploratory data analysis and predictive modeling. It is a method for identifying significant patterns in the provided dataset by lowering the variances. PCA frequently looks for the surface with the lowest dimensionality while projecting the high-dimensional data. The variance of each characteristic is taken into account by PCA since a high attribute indicates a strong divide between groups, which reduces the dimensionality.

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