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Machine Learning for Streaming Data with Python

You're reading from   Machine Learning for Streaming Data with Python Rapidly build practical online machine learning solutions using River and other top key frameworks

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Product type Paperback
Published in Jul 2022
Publisher Packt
ISBN-13 9781803248363
Length 258 pages
Edition 1st Edition
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Author (1):
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Joos Korstanje Joos Korstanje
Author Profile Icon Joos Korstanje
Joos Korstanje
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Introduction and Core Concepts of Streaming Data
2. Chapter 1: An Introduction to Streaming Data FREE CHAPTER 3. Chapter 2: Architectures for Streaming and Real-Time Machine Learning 4. Chapter 3: Data Analysis on Streaming Data 5. Part 2: Exploring Use Cases for Data Streaming
6. Chapter 4: Online Learning with River 7. Chapter 5: Online Anomaly Detection 8. Chapter 6: Online Classification 9. Chapter 7: Online Regression 10. Chapter 8: Reinforcement Learning 11. Part 3: Advanced Concepts and Best Practices around Streaming Data
12. Chapter 9: Drift and Drift Detection 13. Chapter 10: Feature Transformation and Scaling 14. Chapter 11: Catastrophic Forgetting 15. Chapter 12: Conclusion and Best Practices 16. Other Books You May Enjoy

Scaling data for streaming

In the first part of this section, let's start by looking at some solutions for streaming scaling data. Before going into the solutions, let's do a quick recap of what scaling is and how it works.

Introducing scaling

Numerical variables can be of any scale, meaning they can have very high average values or low average values, for example. Some machine learning algorithms are not at all impacted by the scale of a variable, whereas other machine learning algorithms can be strongly impacted.

Scaling is the practice of taking a numerical variable and reducing its range, and potentially its standard deviation, to a pre-specified range. This will allow all machine learning algorithms to learn from the data without problems.

Scaling with MinMaxScaler

To achieve this goal, a commonly used method is the Min-Max scaler. The Min-Max scaler will take an input variable in any range and reduce all of the values to fall in between the range (0 to...

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