What is «principal component analysis»?

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components.

en.wikipedia.org/wiki/Principal_component_analysis

Principal Component Analysis, or PCA, is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.

Reducing the number of variables of a data set naturally comes at the expense of accuracy, but the trick in dimensionality reduction is to trade a little accuracy for simplicity because:

  • smaller data sets are easier to explore and visualize
  • make analyzing data much easier and faster for machine learning algorithms without extraneous variables to process.

Zakaria Jaadi - «A step by step explanation of Principal Component Analysis» (2019-02-28)