How does SVM handle multi-class classification problems?

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Q: How does SVM handle multi-class classification problems?

Solution: SVM can handle multi-class classification using one-vs-one or one-vs-all strategies.

Steps: 5

Step 1: Understand that SVM (Support Vector Machine) is primarily designed for binary classification, which means it can separate data into two classes.
Step 2: To handle multi-class classification (more than two classes), SVM uses strategies like one-vs-one and one-vs-all.
Step 3: In the one-vs-one strategy, SVM creates a separate model for every pair of classes. For example, if there are three classes (A, B, C), it will create models for (A vs B), (A vs C), and (B vs C).
Step 4: In the one-vs-all strategy, SVM creates a model for each class against all other classes. For example, for three classes (A, B, C), it will create models for (A vs not A), (B vs not B), and (C vs not C).
Step 5: After training, when a new data point is given, SVM uses these models to predict the class by either voting (in one-vs-one) or choosing the class with the highest score (in one-vs-all).