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

Chapter 6. Feature Transformations

So far, in this text, we have encountered feature engineering tools from what seems like all possible angles of data. From analyzing tabular data in order to ascertain levels of data to constructing and selecting columns using statistical measures in order to optimize our machine learning pipelines, we have been on a remarkable journey of dealing with features in our data.

It is worth mentioning once more that enhancements of machine learning come in many forms. We generally consider our two main metrics as accuracy and prediction/fit times. This means that if we can utilize feature engineering tools to make our pipeline have higher accuracy in a cross-validated setting, or be able to fit and/or predict data quicker, then we may consider that a success. Of course, our ultimate hope is to optimize for both accuracy and time, giving us a much better pipeline to work with.

The past five chapters have dealt with what is considered classical feature engineering...