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Feature Engineering Made Easy

You're reading from  Feature Engineering Made Easy

Product type Book
Published in Jan 2018
Publisher Packt
ISBN-13 9781787287600
Pages 316 pages
Edition 1st Edition
Languages
Authors (2):
Sinan Ozdemir Sinan Ozdemir
Profile icon Sinan Ozdemir
Divya Susarla Divya Susarla
Profile icon Divya Susarla
View More author details
Toc

Table of Contents (14) Chapters close

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. Introduction to Feature Engineering 2. Feature Understanding – What's in My Dataset? 3. Feature Improvement - Cleaning Datasets 4. Feature Construction 5. Feature Selection 6. Feature Transformations 7. Feature Learning 8. Case Studies 1. Other Books You May Enjoy

How centering and scaling data affects PCA


As with many of the transformations that we have worked with previously in this text, the scaling of features tends to matter a great deal to the transformations. PCA is no different. Previously, we mentioned that the scikit-learn version of PCA automatically centers data in the prediction phase, but why doesn't it do so at the fitting time? If the scikit-learn PCA module goes through the trouble of centering data in the predict method, why doesn't it do so while calculating the eigenvectors? The hypothesis here is that centering data doesn't affect the principal components. Let's test this:

  1. Let's import out StandardScaler module from scikit-learn and center the iris dataset:
# import our scaling module
 from sklearn.preprocessing import StandardScaler
 # center our data, not a full scaling
 X_centered = StandardScaler(with_std=False).fit_transform(iris_X)

 X_centered[:5,]

 array([[-0.74333333, 0.446 , -2.35866667, -0.99866667], [-0.94333333, -0...
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