Multi-class classification problems
In ML, classification problems are ubiquitous. They involve predicting a discrete class label output for an instance. While binary classification – predicting one of two possible outcomes – is a common scenario, many real-world problems require predicting more than two classes. This is where multi-class classification comes into play.
Multi-class classification is a problem where an instance can belong to one of many classes. For example, consider an ML model designed to categorize news articles into topics. The articles could be classified into categories such as Sports, Politics, Technology, Health, and so on. Each of these categories represents a class, and since there are more than two classes, this is a multi-class classification problem.
It’s important to note that each instance belongs to exactly one class in multi-class classification. If each instance could belong to multiple classes, it would be a multi-label...
