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## You're reading fromPython Machine Learning (Wiley)

Product type Book
Published in Apr 2019
Publisher Wiley
ISBN-13 9781119545637
Pages 320 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Wei-Meng Lee

1. Cover
2. Introduction
3. CHAPTER 1: Introduction to Machine Learning 4. CHAPTER 2: Extending Python Using NumPy 5. CHAPTER 3: Manipulating Tabular Data Using Pandas 6. CHAPTER 4: Data Visualization Using matplotlib 7. CHAPTER 5: Getting Started with Scikit‐learn for Machine Learning 8. CHAPTER 6: Supervised Learning—Linear Regression 9. CHAPTER 7: Supervised Learning—Classification Using Logistic Regression 10. CHAPTER 8: Supervised Learning—Classification Using Support Vector Machines 11. CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN) 12. CHAPTER 10: Unsupervised Learning—Clustering Using K‐Means 13. CHAPTER 11: Using Azure Machine Learning Studio 14. CHAPTER 12: Deploying Machine Learning Models 15. Index

# Kernel Trick

Sometimes, the points in a dataset are not always linearly separable. Consider the points shown in Figure 8.11.

You can see that it is not possible to draw a straight line to separate the two sets of points. With some manipulation, however, you can make this set of points linearly separable. This technique is known as the kernel trick. The kernel trick is a technique in machine learning that transforms data into a higher dimension space so that, after the transformation, it has a clear dividing margin between classes of data.

To do so, we can add a third dimension, say the z‐axis, and define z to be:

z = x 2 + y 2 --

Once we plot the points using a 3D chart, the points are now linearly separable. It is difficult to visualize this unless you plot the points out. The following code snippet does just that:

````%matplotlib inline`
` `
`from mpl_toolkits.mplot3d...````
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