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TeacherColab · Free Student Worksheet
Features and Labels in Machine Learning
Student Name
Date
Learning Objective
Understand the two key ingredients of supervised learning: features (the inputs AI looks at) and labels (the answers AI learns to predict).
Section 1 — What do you already know?
Before we begin, write down anything you already know about: Features & Labels
Section 2 — Key Concepts
1. What are features in machine learning?
Answer:
Features are the input characteristics that an AI model uses to make predictions. For example, when predicting house prices, features might include: number of bedrooms, location, floor area, and age of the house.
2. What are labels in machine learning?
Answer:
A label is the answer or output that the model is trying to predict. In our house example, the label is the actual selling price. The model learns to predict labels from features.
3. Why do features matter in AI?
Answer:
The quality and choice of features directly affects how accurate an AI model is. If you include the wrong features — or miss important ones — the model will make poor predictions.
Section 3 — Check Your Understanding
Answer the following questions in your own words.
1. Define features and labels with real-world examples
2. Identify features and labels in a given dataset
3. Explain how features affect prediction quality
4. Understand the concept of feature selection
Section 4 — Fill in the Blanks
Complete the sentences using the concepts you have learned.
1. Features & Labels helps computers _________________ patterns from _________________.
2. The main difference between AI and a normal program is that AI _________________ from data rather than following fixed _________________.
3. One real-world example of Features & Labels is _________________, which uses it to _________________.
4. The inputs used by an AI model are called _________________ and the output it predicts is called a _________________.
Section 5 — Scenario Thinking
🤔 Think about this:
A healthcare organisation wants to use Features & Labels to assist doctors in diagnosing patients earlier. What data would the AI need? What challenges could arise? How can the team ensure the AI is fair, safe, and accurate?
Section 6 — What Did I Learn?
✅ Three key ideas I now understand:
❓ One question I still have:
⭐ Challenge Task
If you were designing an AI solution using Features & Labels, what problem would you solve? Outline a 3-step plan: (1) Problem statement, (2) Data to collect, (3) How to verify accuracy.