Improving a model
Lesson details
Learning outcome
I can make changes to how my model is trained to improve the accuracy of the output.
Key learning points
- Data cleaning can remove outliers so the model "learns" real patterns instead of noise.
- False positives occur when data is misclassified.
- Creating a null class reduces the chances of misclassification.
Keywords
Misclassification - when a model predicts the wrong class for a sample
Data cleaning - the process of detecting and correcting, or removing, corrupt or inaccurate data from a data set
Noise - unwanted or random variations in data that don’t represent meaningful patterns
False positive - a sample that has been incorrectly classified
Common misconception
More data is always better as it means the model can be better informed to make a prediction.
Poorly labelled or inconsistent data can make a model's predictions inaccurate. It is better to use a smaller amount of higher quality data to make more accurate predictions.
Teacher tip
A null class is a concept that learners may find difficult. Model the importance of recording a null class when training a model.
Equipment
Pupils will need access to a physical device to capture data and a means of creating a data-driven application. Examples in this lesson use micro:bits and CreateAI https://oak.link/createai
File needed for this lesson
microbit-Oak 1.29 MB (HEX)
Download this file to use in the lesson.
Licence
Lesson video
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Prior knowledge starter quiz
6 Questions
Q1.A machine learning model can make incorrect predictions if the training data has .
Q2.A model’s score tells us how likely the model’s prediction is correct.
Q3.Match the problem with its potential cause in machine learning.
Q4.A classifier learns by analysing between samples and grouping them into classes.
Q5.What is one way to improve a machine learning model’s accuracy?
Q6.Which of the following best describes an outlier in a data set?
Assessment exit quiz
6 Questions
Q1.What is the main benefit of training an AI model on diverse data?
Q2.Cleaning data enhances the of a machine learning model.
Q3.Match the following terms with their descriptions.
Q4.What is an outlier in data?
Q5.Adding a null class helps prevent in machine learning models
Q6.Why is it important to include data from people with different movement styles when training a model to classify movements?
To help you plan your 9 computing lesson on: Improving a model, download all teaching resources for free and adapt to suit your pupils' needs...
To help you plan your 9 computing lesson on: Improving a model, download all teaching resources for free and adapt to suit your pupils' needs.
The starter quiz will activate and check your pupils' prior knowledge, with versions available both with and without answers in PDF format.
We use learning cycles to break down learning into key concepts or ideas linked to the learning outcome. Each learning cycle features explanations with checks for understanding and practice tasks with feedback. All of this is found in our slide decks, ready for you to download and edit. The practice tasks are also available as printable worksheets and some lessons have additional materials with extra material you might need for teaching the lesson.
The assessment exit quiz will test your pupils' understanding of the key learning points.
Our video is a tool for planning, showing how other teachers might teach the lesson, offering helpful tips, modelled explanations and inspiration for your own delivery in the classroom. Plus, you can set it as homework or revision for pupils and keep their learning on track by sharing an online pupil version of this lesson.
Explore more key stage 3 computing lessons from the Machine learning using the micro:bit unit, dive into the full secondary computing curriculum, or learn more about lesson planning.