What type of machine learning technique is typically used for tasks that involve predicting categories?

Classification is a type of machine learning technique specifically designed for tasks that involve predicting discrete categories or labels. In classification problems, the goal is to assign a given input to one of the predefined classes based on its features.

For example, an application could involve classifying emails as either "spam" or "not spam," or categorizing images as being of cats or dogs. Algorithms used for classification can include logistic regression, decision trees, support vector machines, and neural networks, among others. Each of these techniques utilizes training data, which consists of input-output pairs, to learn the relationships between features and target categories.

In contrast, regression techniques are used for predicting continuous numerical values rather than discrete categories. Unsupervised learning and clustering are focused on finding patterns or structures in data without predefined labels, which differs fundamentally from the goal of classification tasks. Therefore, classification stands out as the appropriate choice for tasks that require predictions of distinct categories.