November 2017
Beginner to intermediate
366 pages
7h 59m
English
Content preview from R Data Analysis Projects
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Normalization
Our next step is to normalize the ratings. Currently, our ratings range from -10 to 10. For some of the underlying algorithms to work efficiently, it is generally a good practice to normalize the data. One reason for normalizing the data is to account for the different biases people use when rating items. Users do not rate items consistently, one user may mainly only recommend items they such as, (that is, mostly 4/5, 5/5) whereas another user may use rate across the range. Normalizing the data accounts for these biases.
Two normalization techniques are available with recommenderlab:
- The first one is centering; it removes the row bias by subtracting the row mean value from all the row values.
- The second one is the z-score normalization; ...
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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.