Book Image

Python Feature Engineering Cookbook

By : Soledad Galli
Book Image

Python Feature Engineering Cookbook

By: Soledad Galli

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.
Table of Contents (13 chapters)

Trimming outliers from the dataset

Trimming, or truncating, is the process of removing observations that show outliers in one or more variables in the dataset. There are three commonly used methods to set the boundaries beyond which a value can be considered an outlier. If the variable is normally distributed, the boundaries are given by the mean plus or minus three times the standard deviation, as approximately 99% of the data will be distributed between those limits. For normally, as well as not normally, distributed variables, we can determine the limits using the inter-quartile range proximity rules or by directly setting the limits to the 5th and 95th quantiles. We covered the formula for the inter-quartile range proximity rule in the Getting ready section of the Highlighting outliers recipe in Chapter 1, Foreseeing Variable Problems...