September 2014
Intermediate to advanced
512 pages
12h 39m
English
Content preview from IPython Interactive Computing and Visualization CookbookBecome 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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Reducing the dimensionality of a dataset with a principal component analysis
In the previous recipes, we presented supervised learning methods; our data points came with discrete or continuous labels, and the algorithms were able to learn the mapping from the points to the labels.
Starting with this recipe, we will present unsupervised learning methods. These methods might be helpful prior to running a supervised learning algorithm. They can give a first insight into the data.
Let's assume that our data consists of points xi without any labels. The goal is to discover some form of hidden structure in this set of points. Frequently, data points have intrinsic low dimensionality: a small number of features suffice to accurately describe the data. ...
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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.