Types of regression models

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).

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^Tx 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 regression uses an identity link (that is, y = w^Tx directly), while binary classification uses alternative link functions as discussed here.

Spark's ML library...