Classification

What is Classification?

Classification is a fundamental task in artificial intelligence and machine learning where an algorithm is trained to recognize patterns in data and assign labels to new, unseen instances based on those patterns. The goal is to map input data to a specific category or class. For example, in a spam email filter, the system is trained to classify incoming emails as either 'spam' or 'not spam'. Classification algorithms use a variety of techniques, such as decision trees, support vector machines, and neural networks, to make these predictions. These systems are trained on labeled datasets, where the correct output (class) is already known, allowing the algorithm to learn the relationship between input features and the target class. Once trained, the model can be used to classify new data with a certain level of accuracy.

In artificial intelligence, classification is the process of predicting the category or class of a given data point from a predefined set of categories.

Examples

• Email Spam Detection: Email services like Gmail use classification algorithms to filter out spam emails from your inbox. The system analyzes various features of the email, such as the sender, subject line, and content, to determine if it should be classified as spam or not.


• Image Recognition: In applications like Google Photos, classification models are used to tag and organize images. For instance, the system can classify images of pets, landscapes, or specific people, making it easier to search and sort through photos.

Additional Information

• Classification can be binary (two classes) or multi-class (more than two classes).


• Accuracy, precision, recall, and F1 score are common metrics used to evaluate the performance of classification models.