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Python Feature Engineering Cookbook

You're reading from   Python Feature Engineering Cookbook A complete guide to crafting powerful features for your machine learning models

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835883587
Length 396 pages
Edition 3rd Edition
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Author (1):
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Soledad Galli Soledad Galli
Author Profile Icon Soledad Galli
Soledad Galli
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Toc

Table of Contents (14) Chapters Close

Preface 1. Chapter 1: Imputing Missing Data FREE CHAPTER 2. Chapter 2: Encoding Categorical Variables 3. Chapter 3: Transforming Numerical Variables 4. Chapter 4: Performing Variable Discretization 5. Chapter 5: Working with Outliers 6. Chapter 6: Extracting Features from Date and Time Variables 7. Chapter 7: Performing Feature Scaling 8. Chapter 8: Creating New Features 9. Chapter 9: Extracting Features from Relational Data with Featuretools 10. Chapter 10: Creating Features from a Time Series with tsfresh 11. Chapter 11: Extracting Features from Text Variables 12. Index 13. Other Books You May Enjoy

Grouping rare or infrequent categories

Rare categories are those present only in a small fraction of the observations. There is no rule of thumb to determine how small a small fraction is, but typically, any value below 5% can be considered rare.

Infrequent labels often appear only on the train set or only on the test set, thus making the algorithms prone to overfitting or being unable to score an observation. In addition, when encoding categories to numbers, we only create mappings for those categories observed in the train set, so we won’t know how to encode new labels. To avoid these complications, we can group infrequent categories into a single category called Rare or Other.

In this recipe, we will group infrequent categories using pandas and feature-engine.

How to do it...

First, let’s import the necessary Python libraries and get the dataset ready:

  1. Import the necessary Python libraries, functions, and classes:
    import numpy as np
    import pandas...
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