July 2016
Beginner to intermediate
462 pages
9h 14m
English
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Recursively eliminating features
If we have many features (explanatory variables), it is tempting to include them all in our model. However, we then run the risk of overfitting—getting a model that works very well for the training data and very badly for unseen data. Not only that, but the model is bound to be relatively slow and require a lot of memory. We have to weigh accuracy (or an other metric) against speed and memory requirements.
We can try to ignore features or create new better compound features. For instance, in online advertising, it is common to work with ratios, such as the ratio of views and clicks related to an ad. Common sense or domain knowledge can help us select features. In the worst-case scenario, we may have to rely on correlations ...
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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.