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TeacherColab · Free Student Worksheet
What Is Classification in Machine Learning?
Student Name
Date
Learning Objective
Classification is how AI sorts things into categories. Learn how decision trees and classifiers work through step-by-step examples.
Section 1 — What do you already know?
Before we begin, write down anything you already know about: Classification
Section 2 — Key Concepts
1. What is classification in machine learning?
Answer:
Classification is a type of machine learning where the model learns to assign input data to one of several categories. For example: is this email spam or not spam? Is this fruit an apple, banana, or mango?
2. What is a decision tree?
Answer:
A decision tree is a classification algorithm that makes decisions by asking a series of yes/no questions. Each question splits the data further until a final category is assigned — like following a flowchart.
3. What are examples of classification AI?
Answer:
Spam filters (spam/not spam), medical diagnosis (disease/no disease), face recognition (which person?), image labelling (cat/dog/bird), and sentiment analysis (positive/negative) are all classification problems.
Section 3 — Check Your Understanding
Answer the following questions in your own words.
1. Define classification and give three real-world examples
2. Explain how a decision tree makes classification decisions
3. Build a simple decision tree for a classification problem
4. Understand binary and multi-class classification
Section 4 — Fill in the Blanks
Complete the sentences using the concepts you have learned.
1. Classification 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 Classification 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 Classification 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 Classification, what problem would you solve? Outline a 3-step plan: (1) Problem statement, (2) Data to collect, (3) How to verify accuracy.