Linear regression tries to fit a line through a given set of points, choosing the best fit. The best fit is the line that minimizes the summed squared difference between the value dictated by the line for a certain value of x and its corresponding y values. (It is optimizing the same squared error that we met before when checking how good a mean was as a predictor.)
Since linear regression is a line; in bi-dimensional space (x, y), it takes the form of the classical formula of a line in a Cartesian plane: y = mx + q, where m is the angular coefficient (expressing the angle between the line and the x axis) and q is the intercept between the line and the x axis.
Formally, machine learning indicates the correct expression for a linear regression as follows:
Here, again, X is a matrix of the predictors, β is a matrix of coefficients, and β0 is a constant value called the bias (it is the same as the Cartesian formulation, only the notation is different).
We can better...
