The linear regression model is characterized by the fact that the data is represented as sums of features, leading to a straight line in the Cartesian plane.
In formal terms, linear regression can be described by the following formula:
Here, y represents the predicted values, which are the result of the linear combination of the single features (represented by the X matrix) to which a weight vector is applied (represented by the w vector), and by the addition of a constant (β), which represents the default predicted value when all features assume the value of zero (or simply are missing).
The β constant ...
