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TensorFlow Machine Learning Cookbook

You're reading from   TensorFlow Machine Learning Cookbook Over 60 recipes to build intelligent machine learning systems with the power of Python

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781789131680
Length 422 pages
Edition 2nd Edition
Languages
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Authors (2):
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Nick McClure Nick McClure
Author Profile Icon Nick McClure
Nick McClure
Sujit Pal Sujit Pal
Author Profile Icon Sujit Pal
Sujit Pal
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Toc

Table of Contents (19) Chapters Close

Title Page
Copyright and Credits
Dedication
Packt Upsell
Contributors
Preface
1. Getting Started with TensorFlow FREE CHAPTER 2. The TensorFlow Way 3. Linear Regression 4. Support Vector Machines 5. Nearest-Neighbor Methods 6. Neural Networks 7. Natural Language Processing 8. Convolutional Neural Networks 9. Recurrent Neural Networks 10. Taking TensorFlow to Production 11. More with TensorFlow 1. Other Books You May Enjoy Index

Implementing deming regression


In this recipe, we will implement deming regression, which means we will need a different way to measure the distance between the model line and data points.

Note

Deming regression goes by several names. It is also known as total regression, orthogonal distance regression (ODR), and shortest-distance regression.

Getting ready

If least squares linear regression minimizes the vertical distance to the line, deming regression minimizes the total distance to the line. This type of regression minimizes the error in both y and x values.

See the following diagram for a comparison:

Figure 8: Difference between regular linear regression and deming regression; linear regression on the left minimizes the vertical distance to the line, and deming regression on the right minimizes the total distance to the line

To implement deming regression, we have to modify the loss function. The loss function in regular linear regression minimizes the vertical distance. Here, we want to minimize...

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