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

Feature understanding – what’s in my dataset?

In our first subtopic, we will start to build our fundamentals in dealing with data. By understanding the data in front of us, we can start to have a better idea of where to go next. We will begin to explore the different types of data out there as well as how to recognize the type of data inside datasets. We will look at datasets from several domains and identify how they are different from each other and how they are similar to each other. Once we are able to comfortably examine data and identify the characteristics of different attributes, we can start to understand the types of transformations that are allowed and that promise to improve our machine learning algorithms.

Among the different methods of understanding, we will be looking at:

  • Structured versus unstructured data
  • The four levels of data
  • Identifying missing data values
  • Exploratory data analysis
  • Descriptive statistics
  • Data visualizations

We will begin at a basic level by identifying the structure of, and then the types of data in front of us. Once we are able to understand what the data is, we can start to fix problems with the data. As an example, we must know how much of our data is missing and what to do when we have missing data.

Make no mistake, data visualizations, descriptive statistics, and exploratory data analysis are all a part of feature engineering. We will be exploring each of these procedures from the perspective of the machine learning engineer. Each of these procedures has the ability to enhance our machine learning pipelines and we will test and alter hypotheses about our data using them.