The classic regression problem involves a single independent variable and a dependent variable. 

Multiple linear regression involves two or more independent variables that contribute to a single dependent variable. 

Problems in which multiple inputs are used to predict a single numeric outcome are also called multivariate linear regression.

Multi-label regression is the task of predicting multiple dependent variables within a single model. 

For example, in multi-label logistic regression, a sample can be assigned to multiple different labels. (This is different from the task of predicting multiple levels within a single class variable.)

Two methods to measure error and fit the regression line: ordinary least squares method, and gradient descent.

• Gradient descent is a method that minimizes the amount of error at each step of the model training process. There are many variations on gradient descent and its optimization for various learning problems has been extensively studied. If you choose this option for Solution method, you can set a variety of parameters to control the step size, learning rate, and so forth. This option also supports use of an integrated parameter sweep.

• Ordinary least squares refers to the loss function, which computes error as the sum of the square of distance from the actual value to the predicted line, and fits the model by minimizing the squared error. This method assumes a strong linear relationship between the inputs and the dependent variable.