In the field of artificial intelligence (AI), classification refers to a type of supervised learning where an algorithm learns to predict the class or category of a given input from a set of pre-defined classes. In a classification task, the AI is trained using labeled data – that is, data that has already been categorized.
For example, let’s consider an email spam detection system. In this case, the two classes could be „spam“ and „not spam“. The system would be trained on a set of emails that have already been labeled as either „spam“ or „not spam“, and it would learn characteristics of emails that tend to be associated with each class. After the training phase, the system would be able to classify new, unseen emails into one of the two classes based on what it has learned.
There are different types of classification, including binary classification (where there are exactly two classes), multi-class classification (where there are more than two classes), and multi-label classification (where each example can belong to more than one class).
Popular classification algorithms include logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, and many types of neural networks.