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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 2. Chapter 2: Encoding Categorical Variables FREE CHAPTER 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

Automatically creating and selecting predictive features from time-series data

In the previous recipe, we automatically extracted several hundred features from time-series variables using tsfresh. If we have more than one time-series variable, we can easily end up with a dataset containing thousands of features. In addition, many of the resulting features had only missing data or were constant and were therefore not useful for training machine learning models.

When we create classification and regression models to solve real-life problems, we often want our models to take a small number of relevant features as input to produce interpretable machine learning outputs. Simpler models have many advantages. First, their output is easier to interpret. Second, simpler models are cheaper to store and faster to train. They also return their outputs faster.

tsfresh includes a highly parallelizable feature selection algorithm based on non-parametric statistical hypothesis tests, which...

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