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A4.3 Machine learning approachesA4.3.3

Role of hyperparameter tuning when evaluating supervised learning algorithms

Explain the role of hyperparameter tuning when evaluating supervised learning algorithms

AHL≈ 60 minCommand term: ExplainPremium

In this lesson

  • Calculating and interpreting accuracy, precision, recall and F1 score
  • Explaining how hyperparameter tuning affects a model's performance
  • Explaining overfitting and underfitting, how to recognise them and how to reduce them

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What's inside

  1. LearnParameters and hyperparameters
  2. LearnMeasuring performance
  3. LabPrecision, recall and a misleading accuracy
  4. CheckQuick check
  5. LearnOverfitting and underfitting
  6. LabTune two algorithms
  7. LearnThe role of tuning in evaluation
  8. CheckWhich problem does each change address?
  9. ExamWorked exam answer
  10. ReviewMistakes examiners see
  11. Exam practiceExam-style questions

Ends with 5 exam-style questions worth 12 marks, with a self-marking markscheme.