#### Overview of this book

Feature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes. This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner. By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production.
Preface
Chapter 3: Transforming Numerical Variables
Chapter 4: Performing Variable Discretization
Chapter 5: Working with Outliers
Chapter 6: Extracting Features from Date and Time Variables
Chapter 7: Performing Feature Scaling
Chapter 8: Creating New Features
Chapter 9: Extracting Features from Relational Data with Featuretools
Chapter 10: Creating Features from a Time Series with tsfresh
Chapter 11: Extracting Features from Text Variables
Index
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# Performing Yeo-Johnson transformation

The Yeo-Johnson transformation is an extension of the Box-Cox transformation that is no longer constrained to positive values. In other words, the Yeo-Johnson transformation can be used on variables with zero and negative values, as well as positive values. These transformations are defined as follows:

• ; if λ ≠ 0 and X >= 0
• ln(X + 1 ); if λ = 0 and X >= 0
• ; if λ ≠ 2 and X < 0
• -ln(-X + 1); if λ = 2 and X < 0

When the variable has only positive values, then the Yeo-Johnson transformation is like the Box-Cox transformation of the variable plus one. If the variable has only negative values, then the Yeo-Johnson transformation is like the BoxCox transformation of the negative of the variable plus one at the power of 2. If the variable has a mix of positive and negative values, the Yeo-Johnson transformation applies different powers to the positive and negative values.

In...