Computer Science · A4.3 Machine learning approaches (HL only)

A4.3.2 — Classification: K-Nearest Neighbours and Decision Trees

Unlock the full notes

Preview

IB DP Computer Science — A4 Machine learning — Machine learning approaches | HL | A4.3.2

📋 What You Will Learn

  • Explain how classification predicts discrete categorical outcomes from labelled data
  • Explain how K-Nearest Neighbours (K-NN) categorises a new data point by distance and majority vote
  • Construct and trace a K-NN classification by hand and in pseudocode, Java and Python
  • Explain how a decision tree classifies by a sequence of feature tests ending at a leaf
  • Describe real-world applications: collaborative-filtering recommendations (K-NN) and medical diagnosis (decision trees)
  • Compare the two techniques and justify a choice for a scenario

🏷️ Why This Matters "You liked the same five films as these users — so you'll probably like what they liked." That is a classifier at work: it looks at labelled examples and assigns a category to something new. Spam/not spam, approve/reject, disease/no disease — a huge share of deployed machine learning is exactly this.

Classification = Supervised Learning for Categories

•

Continue reading the full note

In this topic

More notes on A4.3 Machine learning approaches (HL only)

  1. 01A4.3.3 — Hyperparameter Tuning and Evaluating Supervised Models
  2. 02A4.3.8 — Artificial Neural Networks: Perceptrons to Multi-Layer Networks
  3. 03A4.3.10 — Model Selection and Comparison in Machine Learning
  4. 04A4.3.7 — Genetic Algorithms: Evolution as an Optimiser
  5. 05A4.3.5 — Association Rule Learning: Uncovering Relationships in Data
  6. 06A4.3.9 — Convolutional Neural Networks: Learning Spatial Hierarchies
  7. 07A4.3.6 — Reinforcement Learning: Agents, Rewards and Policies
  8. 08A4.3.1 — Linear Regression: Predicting Continuous Outcomes
  9. 09A4.3.4 — Clustering: Grouping Data by Similarity