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
Contributors
Preface

Chapter 7. Feature Learning

In our final chapter, where we will be exploring feature engineering techniques, we will be taking a look at what is likely the most powerful feature engineering tool at our disposal. Feature learning algorithms are able to take in cleaned data (yes, you still need to do some work) and create brand-new features by exploiting latent structures within data. If all of this sounds familiar, that is because this is the description that we used in the previous chapter for feature transformations. The differences between these two families of algorithms are in the parametric assumptions that they make when attempting to create new features.

We will be covering the following topics:

  • Parametric assumptions of data
  • Restricted Boltzmann Machines
  • The BernoulliRBM
  • Extracting RBM components from MNIST
  • Using RBMs in a machine learning pipeline
  • Learning text features—word vectorization