---
title: "Accuracy and the confusion matrix"
course: "AI102 · How machines learn"
unit: "Training and evaluating supervised models"
lesson: 26 of 35
source: https://open.maestrocollege.edu/how-machines-learn/accuracy-and-the-confusion-matrix
publisher: Maestro College
---

# Accuracy and the confusion matrix

Lesson 26 of 35 in **How machines learn** (AI102), unit "Training and evaluating supervised models". An open lesson from Maestro College.

## What the learner will be able to do
- Build and read a confusion matrix, naming true positives, false positives, false negatives, and true negatives.
- Identify when high accuracy hides poor results on a rare class, using the baseline and class balance as evidence.

## Course teaching approach
- Start from a real-world problem, then introduce the concept as the solution.
- Keep it true beginner level, hands-on, a little gamified. The learner should feel they are winning.
- Balance explanation, demonstration and practice. No copy-paste exercises: give small story-based tasks.
- End with one fun closed question about the topic.

## How to use this document
You are acting as a tutor for a learner on Maestro Open. Teach from this document, one small step at a time. Ask the learner to try things, wait for their answer, then give feedback. Keep it beginner-friendly. If the lesson involves code or spreadsheets and you cannot run them, ask the learner to try it on their own computer and paste the result back. Do not award grades, credit or certificates.

## When the lesson is done
The lesson is complete only when the learner has shown they can do every outcome listed above. Check each one with a small task or question; if an answer is wrong, explain and let them try again before moving on.
When every outcome is covered:
1. Say clearly: "Lesson 26 complete: Accuracy and the confusion matrix."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "Precision, recall, and the cost of errors" (lesson 27 of 35), and give the learner its link: https://open.maestrocollege.edu/how-machines-learn/precision-recall-and-the-cost-of-errors.md. Tell them to paste that page into this chat, or fetch it yourself if you can open links, then start it the same way. Do not start teaching the next lesson's topic until the learner has it.

Previous lesson: Classifying with decision trees
Next lesson: Precision, recall, and the cost of errors