Classification
Classification is the process of assigning data into categories or groups.
In AI and machine learning, it is used to identify what type or class something belongs to.
Why classification is important
Classification helps AI systems:
- Recognize objects
- Detect spam emails
- Identify diseases
- Understand images and text
It is one of the most common tasks in machine learning.
How classification works
The process usually follows these steps:
- The model learns from labeled training data
- It recognizes patterns in the data
- New data is analyzed
- The model assigns the data to a category
The system chooses the most likely class based on learned patterns.
Examples of classification
Email spam detection
The AI classifies emails as:
- Spam
- Not spam
Image recognition
The model classifies images:
- Cat
- Dog
- Car
Medical diagnosis
AI can classify diseases based on symptoms or medical scans.
Binary and multi-class classification
Binary classification
Two possible categories.
Example:
- True / False
- Spam / Not spam
Multi-class classification
More than two categories.
Example:
- Apple
- Banana
- Orange
Classification vs Prediction
- Prediction → estimates a result
- Classification → predicts a specific category
Classification is a specialized form of prediction.
Why learning classification matters
Understanding classification helps you:
- Understand AI decision-making
- Build intelligent systems
- Work with machine learning models
Many real-world AI applications use classification.
A simple example
Think of sorting objects into labeled boxes.
The AI looks at the object and decides which box it belongs in.
Related terms
- What is Machine Learning?
- What is Model?
- What is Neural Network?
Source
Information simplified from the Wikipedia article “Statistical Classification”.