Similarly to winsorization, we can replace the extreme values by values closer to other values in the variable, by determining the maximum and minimum boundaries with the mean plus or minus the standard deviation, or the inter-quartile range proximity rule. This procedure is also called bottom and top coding, censoring, or capping. We can cap both extremes of the distribution or just one of the tails, depending on where we find the outliers in the variable. In this recipe, we will replace extreme values by the mean and standard deviation or the inter-quartile range proximity rule, using pandas, NumPy, and Feature-engine, and using the Boston House Prices dataset from scikit-learn.
Python Feature Engineering Cookbook
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Python Feature Engineering Cookbook
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Overview of this book
Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code.
Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains.
By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems.
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Current Title:
Python Feature Engineering Cookbook
Table of Contents (13 chapters)
Preface
Foreseeing Variable Problems When Building ML Models
Free Chapter
Imputing Missing Data
Encoding Categorical Variables
Transforming Numerical Variables
Performing Variable Discretization
Working with Outliers
Deriving Features from Dates and Time Variables
Performing Feature Scaling
Applying Mathematical Computations to Features
Creating Features with Transactional and Time Series Data
Extracting Features from Text Variables
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