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

A4.3.1 — Linear Regression: Predicting Continuous Outcomes

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IB DP Computer Science — A4 Machine learning — Machine learning approaches | HL | A4.3.1

📋 What You Will Learn

  • Explain the relationship between independent (predictor) and dependent (response) variables
  • Explain the significance of the slope and intercept in the regression equation ŷ = a + bx
  • Construct a least-squares fit by hand and in pseudocode, Java and Python
  • Use the fitted line to predict a continuous outcome for a new input
  • Assess how well the model fits the data using r² (coefficient of determination)
  • Identify the limits of regression: extrapolation, outliers, and correlation vs causation

📈 Why This Matters A tutoring company notices that students who study more tend to score more. If that relationship can be captured as a straight line, the company can predict a likely score from planned study hours — a number, not a category. Turning "tends to" into a usable equation is exactly what linear regression does.

Regression Is Supervised Learning for Numbers

  • Linear

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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. 04A4.3.10 — Model Selection and Comparison in Machine Learning
  5. 05A4.3.7 — Genetic Algorithms: Evolution as an Optimiser
  6. 06A4.3.5 — Association Rule Learning: Uncovering Relationships in Data
  7. 07A4.3.9 — Convolutional Neural Networks: Learning Spatial Hierarchies
  8. 08A4.3.6 — Reinforcement Learning: Agents, Rewards and Policies
  9. 09A4.3.4 — Clustering: Grouping Data by Similarity