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Fundamentals of Machine Learning: A Pythonic Introduction

SVM in Sklearn

Learn how to implement SVM in sklearn using the one-vs-all approach for multiclass classification.

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Support Vector Machines (SVMs) are naturally designed for binary classification, where the goal is to separate data into two classes using the maximum-margin decision boundary. However, many real-world problems involve more than two classes, which requires extending the binary SVM framework. In this lesson, we explore how SVM handles multiclass classification, the common one-vs-all strategy, and how to implement it using scikit-learn.

Multiclass classification

SVMs are often used for binary classification, where the goal is to separate data into two classes. However, SVMs can also be used for multiclass classification, where the goal is to separate data into more than two classes.

Multiclass SVM
Multiclass SVM

The above illustration shows the multiclass classification of the Iris dataset using SVM.

One-vs-all

Multiclass SVM is an extension of binary SVM, where the SVM is trained to classify data into more than two classes. There are several approaches to training a multiclass SVM, but the most common approach is to use the one-vs-all (OVA) method. In the OVA method, a separate binary SVM is trained for each class, with the data of that ...