The core idea of linear models (or generalized linear models) is that we model the predicted outcome of interest (often called the target or dependent variable) as a function of a simple linear predictor applied to the input variables (also referred to as features or independent variables).

*y = f(w*

^{T}x)Here, *y* is the target variable, *w* is the vector of parameters (known as the weight vector), and *x* is the vector of input features.

*w ^{T}x* is the linear predictor (or vector dot product) of the weight vector

*w*and feature vector

*x*. To this linear predictor, we applied a function

*f*(called the link function).

Linear models can, in fact, be used for both classification and regression simply by changing the link function. ...