Book Image

Feature Engineering Made Easy

By : Sinan Ozdemir, Divya Susarla
Book Image

Feature Engineering Made Easy

By: Sinan Ozdemir, Divya Susarla

Overview of this book

Feature engineering is the most important step in creating powerful machine learning systems. This book will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective. You will start with understanding your data—often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You'll also learn how to use machine learning on your machines, automatically learning amazing features for your data. By the end of the book, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization.
Table of Contents (14 chapters)
Title Page
Copyright and Credits
Packt Upsell

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)


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