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Regression Analysis with Python by Alberto Boschetti, Luca Massaron

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Minimizing the cost function

At the core of linear regression, there is the search for a line's equation that it is able to minimize the sum of the squared errors of the difference between the line's y values and the original ones. As a reminder, let's say our regression function is called h, and its predictions h(X), as in this formulation:

Minimizing the cost function

Consequently, our cost function to be minimized is as follows:

Minimizing the cost function

There are quite a few methods to minimize it, some performing better than others in the presence of large quantities of data. Among the better performers, ...

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