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 kernel PCA for nonlinear dimensionality reduction
Most of the techniques in statistics are linear by nature, so in order to capture nonlinearity, we might need to apply some transformation. PCA is, of course, a linear transformation. In this recipe, we'll look at applying nonlinear transformations, and then apply PCA for dimensionality reduction.
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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.