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A4.3 Machine learning approachesA4.3.2

Classification techniques in supervised learning

Explain how classification techniques in supervised learning are used to predict discrete categorical outcomes

AHL≈ 65 minCommand term: ExplainPremium

In this lesson

  • Explaining how K-nearest neighbours classifies a new data point
  • Explaining how a decision tree is built from labelled data and used to classify
  • Explaining real-world applications: collaborative filtering (K-NN) and medical diagnosis (decision trees)

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What's inside

  1. LearnPredicting a category
  2. LearnK-nearest neighbours (K-NN)
  3. LabClassify a fruit
  4. CheckQuick check
  5. LearnStrengths and weaknesses of K-NN
  6. LearnDecision trees
  7. LabBuild a diagnosis tree
  8. CheckQuick check
  9. LearnStrengths and weaknesses of decision trees
  10. LearnReal-world applications
  11. LabRecommend a book
  12. CheckMatch each term to its meaning
  13. ExamWorked exam answer
  14. ReviewMistakes examiners see
  15. Exam practiceExam-style questions

Ends with 5 exam-style questions worth 13 marks, with a self-marking markscheme.