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.

*wTx* 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. Standard linear ...