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(WTx)
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 ...
