Classification

TL;DR

• Organizes data points into discrete classes using their features.
• Requires training on a labeled dataset to predict classes for new, unlabeled data.
• Commonly evaluated with metrics such as accuracy and precision.

Definition

Classification is the process of organizing and grouping data into categories based on shared characteristics or features. It is a common method used in machine learning and data analysis to group data points into distinct classes.

Explanation

Classification assigns each data point to one of a set of predefined classes by examining its features. The typical workflow trains an algorithm on a labeled dataset (where each data point’s class is known) and then uses the trained model to predict classes for new, unlabeled data. Performance of a classification algorithm is typically measured using metrics such as accuracy and precision.

Examples

Email spam detection

Data points are emails and the classes are either spam or not spam. The algorithm uses features of the emails — such as the sender, subject line, and content — to determine which class each email belongs to. This helps automatically filter out unwanted spam emails and improve the user’s email experience.

Image recognition

Data points are images and the classes are the objects or scenes depicted in the images. The algorithm uses features of the images — such as color, texture, and shape — to determine which class each image belongs to. This helps automatically identify and categorize objects in images, with practical applications such as security and surveillance systems.

Use cases

• Machine learning and data analysis tasks that require grouping or categorizing data.
• Practical applications including security and surveillance systems and automated email filtering.

Related terms

• Labeled dataset
• Features
• Algorithm
• Classes
• Accuracy
• Precision