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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

What Is a Support Vector Machine?

In the previous chapter, you saw how to perform classification using logistics regression. In this chapter, you will learn another supervised machine learning algorithm that is also very popular among data scientists—Support Vector Machines (SVM). Like logistics regression, SVM is also a classification algorithm.

The main idea behind SVM is to draw a line between two or more classes in the best possible manner (see Figure 8.1).

Once the line is drawn to separate the classes, you can then use it to predict future data. For example, given the snout length and ear geometry of a new unknown animal, you can now use the dividing line as a classifier to predict if the animal is a dog or a cat.

In this chapter, you will learn how SVM works and the various techniques you can use to adapt SVM for solving nonlinearly‐separable datasets.

Maximum Separability

How does SVM separate two or more...

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