Advanced Machine Learning with R

Dimensionality reduction

Feature reduction (or feature selection) or dimensionality reduction is the process of reducing the input set of independent variables to obtain a lesser number of variables that are really required by the model to predict the target.

In certain cases, it is possible to represent multiple dependent variables by combining them together without losing much information. For example, instead of having two independent variables such as the length of a rectangle and the breath of a rectangle, the dimensions can be represented by only one variable called the area that represents both the length and breadth of the rectangle.

The following mentioned are the multiple reasons we need to perform a dimensionality reduction on ...