Classification and Regression
Classification predicts categories; regression predicts numbers. The distinction determines the model output, evaluation metrics and operational decisions.
Learning objectives
What you will be able to do
- Explain classification and regression accurately in your own words.
- Recognise how the concept appears in real AI products and professional work.
- Identify an important limitation, risk or evaluation requirement.
- Apply the concept through a practical activity and knowledge check.
Core explanation
Build the right mental model
Binary classification chooses between two categories, multiclass classification chooses one of several, and multilabel classification can assign several categories at once. Regression estimates a continuous quantity.
Classification models often output probabilities. A threshold turns the score into an action, so the operational policy is as important as the model. Regression error should be examined across the range—not only as one average.
Some problems can be framed in more than one way. Predicting exact delivery minutes is regression; predicting late/on-time is classification. Choose the framing that supports the actual decision.
Binary classification
Two possible classes, such as attack or normal.
Multiclass classification
One category selected from three or more.
Regression
A numeric prediction such as cost or temperature.
Problem framing
Translating a real need into a precise ML task.
Hospital waiting time
A regression model estimates minutes; a classifier predicts whether a target time will be exceeded.
Apply your learning
Practical activity
- Take three business questions.
- Frame each as classification and as regression where possible.
- Select the version that leads to a useful action.
Write your answers in a learning journal. The value comes from connecting the concept to your own profession.
Knowledge check
Test your understanding
Q1What does regression predict?
Answer: A continuous numerical value.
Q2Why is problem framing important?
Answer: A technically accurate output is useless if it does not support the decision people need to make.
If you cannot explain the answer without reading it, revisit the core explanation and example before continuing.
Continue with authoritative sources
Important industry resources
These links lead to official organisations, industry laboratories or established open-source learning projects.