---
title: "Tuning by minimizing error"
course: "AI101 · AI, explained"
unit: "Learning from data with math"
lesson: 29 of 36
source: https://open.maestrocollege.edu/ai-explained/tuning-by-minimizing-error
publisher: Maestro College
---

# Tuning by minimizing error

Lesson 29 of 36 in **AI, explained** (AI101), unit "Learning from data with math". An open lesson from Maestro College.

## What the learner will be able to do
- Understand error as a single number a learning system tries to make as small as possible, measured on data set aside for tuning.
- Find the best value of a setting by computing the error for each candidate value and choosing the lowest point.
- Identify why the lowest error on the data used to fit a system can mislead, and why tuning needs its own split separate from the final test.

## Teaching notes
STARTING STATE: the k-NN and Bayes classifiers, unmeasured on test. IN THIS LESSON: the tuning split is Unit 2's held-out idea again, inside the training tickets; test untouched. split_training() is given in the materials (seeded shuffle; 180 fit, 60 tuning): read it, don't write it. Tuning error (wrong out of 60) for knn_classify (examples=fit) at k = 1, 3, 5, 9 plus one Bayes line; then k=1's error on its fit tickets (zero: each finds itself). Create an illustration with the math visual: tuning error against k, lowest point marked. classify(text) is the winner with all 240 tickets, measured once on test. END STATE: committed as 'Tune and choose classifier', tagged classifier-final.

## 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 29 complete: Tuning by minimizing error."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "Challenge: can the classifier beat your rules?" (lesson 30 of 36), and give the learner its link: https://open.maestrocollege.edu/ai-explained/challenge-can-the-classifier-beat-your-rules.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: Building a Bayes classifier
Next lesson: Challenge: can the classifier beat your rules?