Machine Learning Algorithms

A bidimensional example

Let's consider a small dataset built by adding some uniform noise to the points belonging to a segment bounded between -6 and 6. The original equation is: y = x + 2 + n, where n is a noise term.

In the following figure, there's a plot with a candidate regression function:

As we're working on a plane, the regressor we're looking for is a function of only two parameters:

In order to fit our model, we must find the best parameters and to do that we choose an ordinary least squares approach. The loss function to minimize ...