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

A4.3.10 — Model Selection and Comparison in Machine Learning

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IB DP Computer Science — A4 Machine learning — Machine learning approaches | HL | A4.3.10

📋 What You Will Learn

  • Explain why different algorithms can yield different results on the same problem
  • Match problem types (continuous, categorical, unlabelled, sequential, optimisation) to appropriate model families
  • Explain the factors behind choosing one model over another: nature of the problem, complexity, desired outcomes
  • Describe how data characteristics (size, dimensionality, noise, linearity, balance) change algorithm performance
  • Compare two models fairly using held-out test data and metrics such as accuracy, precision, recall and F1

🚗 Why This Matters Nobody asks "what is the best vehicle?" — a race needs a sports car, a house move needs a van, a flooded road needs a boat. Machine learning models are the same: there is no universally best algorithm, only the best match between a model, the data you have, and the outcome you need. Choosing well is itself a core ML skill.

Why Different Algorithms Give Different Results

  • Every algorithm makes assumptions about the data: linear regression assumes a

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

  1. 01A4.3.2 — Classification: K-Nearest Neighbours and Decision Trees
  2. 02A4.3.3 — Hyperparameter Tuning and Evaluating Supervised Models
  3. 03A4.3.8 — Artificial Neural Networks: Perceptrons to Multi-Layer Networks
  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