Features, Labels and Models
Features describe each example; labels state what a supervised model should predict; the model learns a relationship between them.
Learning objectives
What you will be able to do
- Explain features, labels and models 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
Feature engineering converts raw information into signals a model can use. A date might become day of week, a network log might become connection rate, and free text might become an embedding.
Features should be available at prediction time, reliably measured and appropriate for the decision. A proxy feature can reproduce sensitive attributes and create indirect discrimination.
A model is the learned function, not the complete AI system. The system also includes data collection, preprocessing, thresholds, interfaces, monitoring and human procedures.
Example
One record or case supplied to a model.
Feature
An input attribute used to make a prediction.
Label
The target outcome in supervised learning.
Model
The learned mapping from features to an output.
Loan repayment prediction
Features describe applicant circumstances and the label records past repayment.
Apply your learning
Practical activity
- Choose a prediction problem and list ten possible features.
- Remove any unavailable at prediction time.
- Mark potential proxies for sensitive information.
- Define the label precisely.
Write your answers in a learning journal. The value comes from connecting the concept to your own profession.
Knowledge check
Test your understanding
Q1Can a feature be accurate but inappropriate?
Answer: Yes. It may leak future information, violate privacy or act as an unfair proxy.
Q2Is the model the whole AI system?
Answer: No. Data pipelines, interfaces, monitoring, policies and people are also part of the system.
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.