This chapter introduces some key concepts of support vector machines through a simple 1-dimensional example. Students are also walked through the  creation of a linearly separable dataset that is used in the subsequent chapter.

Sugar content of soft drinks

Visualizing a sugar content dataset

Identifying decision boundaries

Find the maximal margin separator

Visualize the maximal margin separator

Generating a linearly separable dataset

Generate a 2d uniformly distributed dataset.

Create a decision boundary

Introduce a margin in the dataset

Introduction

Introduces students to the basic concepts of support vector machines by applying the svm algorithm to a dataset that is linearly separable. Key concepts are illustrated through ggplot visualisations that are built from the outputs of the algorithm and the role of the cost parameter is highlighted via a simple example.  The chapter closes with a section on how the algorithm deals with multiclass problems.

Linear Support Vector Machines

Creating training and test datasets

Building a linear SVM classifier

Exploring the model and calculating accuracy

Visualizing Linear SVMs

Visualizing support vectors using ggplot

Visualizing decision & margin bounds using `ggplot2`

Visualizing decision & margin bounds using `plot()`

Tuning linear SVMs

Tuning a linear SVM

Visualizing decision boundaries and margins

When are soft margin classifiers useful?

Multiclass problems

A multiclass classification problem

Iris redux - a more robust accuracy.

Support Vector Classifiers - Linear Kernels

Provides an introduction to polynomial kernels via a dataset that is radially separable (i.e. has a circular decision boundary). After demonstrating the inadequacy of linear kernels for this dataset, students will see how a simple transformation renders the problem linearly separable thus motivating an intuitive discussion of the kernel trick. Students will then apply the polynomial kernel to the dataset and tune the resulting classifier.

Generating a radially separable dataset

Generating a 2d radially separable dataset

Visualizing the dataset

Linear SVMs on radially separable data

Linear SVM for a radially separable dataset

Average accuracy for linear SVM

The kernel trick

Visualizing transformed radially separable data

SVM with polynomial kernel

Tuning SVMs

Using `tune.svm()`

Building and visualizing the tuned model

Polynomial Kernels

Builds on the previous three chapters by introducing the highly flexible Radial Basis Function (RBF) kernel. Students will create a "complex" dataset that shows up the limitations of polynomial kernels. Then, following an intuitive motivation for the RBF kernel, students see how it addresses the shortcomings of the other kernels discussed in this course.

Generating a complex dataset

Generating a complex dataset - part 1

Generating a complex dataset - part 2

Motivating the RBF kernel

Linear SVM for complex dataset

Quadratic SVM for complex dataset

The RBF Kernel

Polynomial SVM on a complex dataset

RBF SVM on a complex dataset

Tuning an RBF kernel SVM

Radial Basis Function Kernels

This course will introduce a powerful classifier, the support vector machine (SVM) using an intuitive, visual approach. Support Vector Machines in R will help students develop an understanding of the SVM model as a classifier and gain practical experience using R’s libsvm implementation from the e1071 package. Along the way, students will gain an intuitive understanding of important concepts, such as hard and soft margins, the kernel trick, different types of kernels, and how to tune SVM parameters. Get ready to classify data with this impressive model.

Introduction to R

This course will introduce the support vector machine (SVM) using an intuitive, visual approach.

Support Vector Machines in R

Machine Learning Scientist in R

Supervised Machine Learning in R

Likely to Recommend

Using `tune.svm()`

“Support Vector Machines in R”

Exercise instructions

Hands-on interactive exercise

Support Vector Machines in R

Chapter 1: Introduction

Chapter 2: Support Vector Classifiers - Linear Kernels

Chapter 3: Polynomial Kernels

Chapter 4: Radial Basis Function Kernels

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