Machine Learning

PCA

Rotates data onto the axes that carry the most variance, so you can drop the rest.

Principal component analysis finds the directions along which your data varies most and re-expresses every point in terms of them. Keep the first handful of components and you often retain most of the signal at a fraction of the width.

It is used for compression, for visualising high-dimensional data in two dimensions, and for removing correlated noise before modelling. The cost is interpretability: a component is a blend of original features and rarely has a clean name.

Also in Machine Learning

Supervised Learning Unsupervised Learning K-Means K-Nearest Neighbours Logistic Regression Linear Regression

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