IB Computer Science 2027 – A4.3.3 Model Evaluation & Hyperparameters – Lesson & Worksheet

IB DP Computer Science A4.3.3 Model Evaluation & Hyperparameters lesson and worksheet for the new 2027 syllabus. A classroom-ready resource covering how machine learning models are evaluated and how hyperparameters affect model performance.

IB Computer Science 2027 – A4.3.3 Model Evaluation & Hyperparameters

A classroom-ready lesson resource for IB Diploma Programme Computer Science, aligned with the new IB Computer Science syllabus for first assessment in May 2027.

What is included

• Lesson presentation
• Student worksheet
• Classroom activities
• Student tasks
• Questions to check understanding
• Mark scheme / answers

Syllabus coverage

A4.3 Machine Learning Approaches

A4.3.3 Model Evaluation & Hyperparameters

Students explore how machine learning models are evaluated and how hyperparameters can be adjusted to improve model performance.

Topics include:

• Model evaluation
• Training and testing
• Accuracy
• Model performance
• Overfitting and underfitting
• Hyperparameters
• The effect of hyperparameter choices on a model
• Comparing machine learning models

Students apply their understanding to machine learning scenarios and evaluate the effectiveness of different models.

Suitable for

• IB Diploma Programme Computer Science
• SL and HL
• New 2027 syllabus
• First assessment May 2027
• Secondary students aged 16–18

The resource can be used as a standalone lesson or incorporated into an existing IB Computer Science scheme of work.

Please note: This is an original teaching resource and is not affiliated with or endorsed by the International Baccalaureate Organisation.