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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

Chapter 7: Online Regression

After looking at online anomaly detection and online classification throughout the previous chapters, there is one large category of online machine learning that remains to be seen. Regression is the family of supervised machine learning models that applies to use cases in which the target variable is numerical.

In anomaly detection and classification, you have seen how to build models to predict categorical targets (yes/no and iris species), but you have not yet seen how to work with a target that is numerical. Working with numerical data requires having methods that work differently, both in the deeper layers of model training and model definition and also in our use of metrics.

Imagine being a weather forecaster trying to forecast the temperature (Celsius) for tomorrow. Maybe you expect a sunny day, and you have a model that you use to predict a temperature of 25 degrees Celsius. Imagine if the next day, you observe that it is cold and only 18...

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