November 2017
Intermediate to advanced
374 pages
10h 19m
English
Content preview from scikit-learn Cookbook , Second Edition - Second Edition
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Using ridge regression to overcome linear regression's shortfalls
In this recipe, we'll learn about ridge regression. It is different from vanilla linear regression; it introduces a regularization parameter to shrink coefficients. This is useful when the dataset has collinear factors.
Ridge regression is actually so powerful in the presence of collinearity that you can model polynomial features: vectors x, x2, x3, ... which are highly collinear and correlated.
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.