Last time we took a look at linear regression which finds the linear function that minimises the differences between its results and values at a set of points that are presumed, possibly after applying some specified transformation, to be random deviations from a straight line or, in multiple dimensions, a flat plane. The purpose was to reveal the underlying relationship between the independent variable represented by the points and the dependent variable represented by the values at them.

This time we shall see how we can approximate the function that defines the relationship between them without actually revealing what it is.

This time we shall see how we can approximate the function that defines the relationship between them without actually revealing what it is.

Full text...