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

Principal Component Analysis


Principal Component Analysis is a technique that takes datasets that have several correlated features and projects them onto a coordinate (axis) system that has fewer correlated features. These new, uncorrelated features (which I referred to before as a super-columns) are called principal components. The principal components serve as an alternative coordinate system to the original feature space that requires fewer features and captures as much variance as possible. If we refer back to our example with the cameras, the principal components are exemplified by the cameras themselves.

Put another way, the goal of the PCA is to identify patterns and latent structures within datasets in order to create new columns and use these columns instead of the original features. Just as in feature selection, if we start with a data matrix of size n x d where n is the number of observations and d is the number of original features, we are projecting this matrix onto a matrix...