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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Summary
In this chapter, we have carried on introducing linear regression, extending our example from a simple to a multiple one. We have revisited the previous outputs from the Statsmodels linear functions (the classical statistical approach) and gradient descent (the data science engine).
We started experimenting with models by removing selected predictors and evaluating the impact of such a move from the point of view of the R-squared measure. Meanwhile we also discovered reciprocal correlations between predictors and how to render more linear relations between each predictor and the target variable by intercepting the interactions and by means of polynomial expansion of the features.
In the next chapter, we will progress again and extend the ...
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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.