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TeacherColab · Free Student Worksheet
Model Accuracy: How Good Is Your AI?
Student Name
Date
Learning Objective
Learn how to measure how well an AI model performs — accuracy percentages, true/false positives, and how to read a confusion matrix.
Section 1 — What do you already know?
Before we begin, write down anything you already know about: Model Accuracy
Section 2 — Key Concepts
1. What is model accuracy in machine learning?
Answer:
Model accuracy is the percentage of predictions the model gets correct. For example, if a model correctly classifies 87 out of 100 emails as spam or not spam, its accuracy is 87%.
2. What is a confusion matrix?
Answer:
A confusion matrix is a table that shows how many predictions were correct and how many were wrong, broken down by category. It reveals not just how many mistakes the model made, but what kind of mistakes.
3. What is a false positive?
Answer:
A false positive is when the model predicts something is true, but it is actually false. For example, a spam filter that marks a legitimate email as spam has made a false positive.
Section 3 — Check Your Understanding
Answer the following questions in your own words.
1. Calculate accuracy as a percentage of correct predictions
2. Define true positives, false positives, true negatives, and false negatives
3. Read and interpret a simple confusion matrix
4. Explain why high accuracy does not always mean a good model
Section 4 — Fill in the Blanks
Complete the sentences using the concepts you have learned.
1. Model Accuracy 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 Model Accuracy 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 Model Accuracy 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 Model Accuracy, what problem would you solve? Outline a 3-step plan: (1) Problem statement, (2) Data to collect, (3) How to verify accuracy.