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Year 9

Training and testing a model

I can train a machine learning model and test the model’s predictions.

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New
New
Year 9

Training and testing a model

I can train a machine learning model and test the model’s predictions.

Download all resources
Share activities with pupils

These resources will be removed by end of Summer Term 2025.

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These resources were created for remote use during the pandemic and are not designed for classroom teaching.

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Lesson details

Key learning points

  1. A classifier's predictions are only as accurate as their training data allows.
  2. Classifiers can still make predictions with a high confidence score, even if the training data is poor.
  3. Causes of poor training data include: outliers, limited training data and poorly labelled data.
  4. It is important to test a classifier to make sure the outputs are as expected.

Keywords

  • Training data - a set of examples used to teach a machine learning model how to recognise patterns

  • Prediction - the output that a model generates based on the patterns determined from the training data

  • Confidence - a measure of how likely the output of a model is correct

  • Bias - error in a machine learning model that causes the consistent output of certain types of outcomes

  • Outlier - a data point that is significantly different from the rest of the data

Common misconception

More training makes a perfect model.

Not neccessarily - models can become too specialised and fail on new data.


To help you plan your year 9 computing lesson on: Training and testing a model, download all teaching resources for free and adapt to suit your pupils' needs...

File needed for this lesson

  • microbit-Oak 1.29 MB (HEX)

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It is important to point out to pupils that classifiers can still make predictions with a high confidence score even if the training data is poor.
Teacher tip

Equipment

Pupils will need micro:bits for this lesson and access to a device that can access CreateAI online.

Licence

This content is © Oak National Academy Limited (2025), licensed on Open Government Licence version 3.0 except where otherwise stated. See Oak's terms & conditions (Collection 2).

Lesson video

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Starter quiz

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6 Questions

Q1.
Why is it important to hold the micro:bit in the same position when recording movement samples?
to make the device look more organised
Correct answer: to ensure data is consistent and reduce variability
to speed up the recording process
to randomly change the motion values for more variety
Q2.
A approach involves using patterns in data rather than predefined rules.
Correct answer: data-driven
rule-based
Q3.
Which axis changes the most when moving a device side to side?
Correct answer: x-axis
y-axis
z-axis
Q4.
A is a collection of similar data samples grouped together under a label.
Correct Answer: class
Q5.
A data-driven system requires a large number of to learn patterns.
Correct Answer: samples
Q6.
Why do we need to collect multiple samples for a model?
to introduce random variations in the data set
Correct answer: to make sure the model identifies consistent patterns
to make the program run slower
so the computer has more numbers to store

Exit quiz

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6 Questions

Q1.
What is training data in machine learning?
the final output of a model
the process of deleting unwanted data
data collected after testing the model
Correct answer: a set of examples used to teach a model how to recognise patterns
Q2.
A classifier uses in a model to predict which class new data belongs to.
Correct Answer: boundaries
Q3.
What is an outlier in a data set?
Correct answer: a data point that is significantly different from the rest of the data
a value that is exactly the same as all others
the most frequently occurring value in the data set
a missing value that does not affect model performance
Q4.
What is a classifier in machine learning?
a type of program that stores data
Correct answer: a machine learning algorithm that groups data into categories
a system that erases training data after use
a way to manually sort data into a spreadsheet
Q5.
Match the term with its definition.
Correct Answer:classifier,a model that sorts data into categories

a model that sorts data into categories

Correct Answer:training data,a set of examples used to teach an AI model

a set of examples used to teach an AI model

Correct Answer:prediction,the model's output based on recognised patterns

the model's output based on recognised patterns

Correct Answer:outlier,a data point that differs significantly from the rest

a data point that differs significantly from the rest

Q6.
Why is it important to test a model with different people?
to make the model more complicated
to see if the computer makes mistakes on purpose
Correct answer: to ensure the AI can adapt to different movement styles and reduce bias
to collect random results without a clear purpose