Year 9 - Computing
Training and testing a model
Machine learning using the micro:bit
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This pupil-friendly study guide includes a unit summary, clear notes, common mistakes, and quick questions with answers.
Unit Summary
This unit is designed to develop pupils programming skills in a new engaging context: physical computing, using the BBC micro:bit. They will get acquainted with the host of components built into the micro:bit, and write simple programs that use these components to interact with the physical world.
Lesson Summary
You will learn to train a machine learning model and test the model's predictions.
Key Notes
- A classifier's predictions are only as accurate as their training data allows.
- Classifiers can still make predictions with a high confidence score, even if the training data is poor.
- Causes of poor training data include: outliers, limited training data and poorly labelled data.
- It is important to test a classifier to make sure the outputs are as expected.
Vocabulary To Learn
- 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: to disproportionately favour one side, group, or outcome over others
- outlier: a data point that significantly differs from the rest of the data in a data set
Common Mistakes To Avoid
- More training makes a perfect model.
3 Quick Questions (With Answers)
1. Describe the system or process from this lesson in clear steps.
A classifier's predictions are only as accurate as their training data allows. Classifiers can still make predictions with a high confidence score, even if the training data is poor.
2. Define this computing term and give one practical example. 'training data'
a set of examples used to teach a machine learning model how to recognise patterns. Add one real device or system example to prove understanding.
3. Correct this common computing misconception.
Mistake: More training makes a perfect model. Correction: Not neccessarily - models can become too specialised and fail on new data.
More Lessons In This Unit
Browse all guides in the Year 9 Computing guide library.