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

The BernoulliRBM

The only scikit-learn implemented version of a Restricted Boltzmann Machine is called BernoulliRBM because it imposes a constraint on the type of probability distribution it can learn. The Bernoulli distribution allows for data values to be between zero and one. The scikit-learn documentation states that the model assumes the inputs are either binary values or values between zero and one. This is done to represent the fact that the node values represent a probability that the node is activated or not. It allows for quicker learning of feature sets. To account for this, we will alter our dataset to account for only hardcoded white/black pixel intensities. By doing so, every cell value will either be zero or one (white or black) to make learning more robust. We will accomplish this in two steps:

  1. We will scale the values of the pixels to be between zero and one
  2. We will change the pixel values in place to be true if the value is over 0.5, and false otherwise

Let's start by scaling...