PCA

Data that has many features or variables, sometimes with the number of variables even exceeding the number of samples, is often referred to as high-dimensional data. High-dimensional data may present challenges for computation, data management, and collinearity. Therefore, a common approach is to reduce the number of dimensions.

When a linear model has two or more highly correlated predictor variables, it is often said to suffer from multicollinearity. The danger of multicollinearity is that estimated regression coefficients can be highly uncertain and possibly nonsensical (e.g., getting a negative coefficient that common sense dictates should be positive). Multicollinearity is usually detected using variance inflation factors (VIF).