February 2016
Beginner to intermediate
312 pages
7h 23m
English
Content preview from Regression Analysis with PythonBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Revisiting gradient descent
In the previous chapter, we introduced the gradient descent technique to speed up processing. As we've seen with Linear Regression, the fitting of the model can be made in two ways: closed form or iterative form. Closed form gives the best possible solution in one step (but it's a very complex and time-demanding step); iterative algorithms, instead, reach the minima step by step with few calculations for each update and can be stopped at any time.
Gradient descent is a very popular choice for fitting the Logistic Regression model; however, it shares its popularity with Newton's methods. Since Logistic Regression is the base of the iterative optimization, and we've already introduced it, we will focus on it in this section. ...
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