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4.2 Classification Algorithms

Classification is a fundamental type of supervised learning where the target variable is categorical, meaning it belongs to a predefined set of classes or categories. In classification problems, the primary objective is to develop a model that can accurately predict the correct class or category for each input sample based on its features. This process involves training the model on a labeled dataset, where each example is associated with its corresponding class label.

To illustrate, consider an email classification system. Given a set of features about an email (such as the subject line, body content, sender information, and metadata), the goal would be to classify it as either spam or not spam. This binary classification ...