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Logistic regression
Term Key terms bank Y12 Software Automation
Definition
Predicts the probability of a category (yes or no) and so is used for classification, not for predicting a number.
Example: Predict whether an email is spam.
Found in: syllabus, course specifications, 2025 HSC marking guidelines, school notes.
Classification (yes/no) using probability.
Where it is used in the test
Syllabus area: Y12 Software Automation.
Question types it can appear in
- Multiple choice: 2025 Q12: true or false statements
- Short answer: Define, outline or explain it and tie it to the scenario.
- Python: Write or complete it in the online Python tool.
Where it has appeared in the paper
Software Automation (Part A). Part letters follow each paper’s own layout.
Related past questions
- 2025 HSC Q12 (2 marks): Logistic regression Software Automation (Part A) · 2 marks
These are the questions the flashcard set links to this term. Open each one and compare your answer with its marking criteria.
Study links
- Flashcard: Logistic regression in Software Automation (Year 12) · study that set
- Memorise: Decision trees
- Key terms bank entry: Logistic regression
- Notes: Describe types of algorithms associated with ML, including linear regression, logistic… (Software Automation (Year 12))
- Notes: Design, develop and apply ML regression models using an OOP to predict numeric values,… (Software Automation (Year 12))
- Master answer: hsc2025-q12 (2 marks, multi-select (true/false check boxes)) (Master model answers: Software Automation)
- Notes: Regression models and KNN (Software Automation (Year 12))
How to use it in an answer
A model sentence
“Predicts the probability of a category (yes or no) and so is used for classification, not for predicting a number.” Then apply it: Predict whether an email is spam.
In a short answer, give the definition in your own words first, then tie it to the situation in the question. Do not copy the example above unless it fits the scenario.
Command words used with it
No linked question shows the command word for this term. Across the past short answer and python questions in this bank the words used most are: Explain, Outline, Describe, Compare, Classify, Analyse. Check the actual word in your question.
Technique for the question types
- Multiple choice: Read all four options, rule out wrong ones, pick one; no penalty for guessing so never leave blank.
- Short answer: One clear point per mark, use the stimulus and course terms, and answer the command word.
- Python: Write code that runs: indentation, quotes, define functions before calling them, print the required output, meet every listed requirement.
What the marking guidelines and feedback say
Logistic regression is for classification; linear and polynomial regression predict continuous values (course specifications).
Worked answers from the marking guidelines
2025 HSC Q12 (2 marks): Logistic regression (2 marks): criteria and sample answer
Criteria
2: True or false correct for all SIX check boxes. 1: Correct for FOUR of the six.
Sample answer
True: spam, two-party election outcome, university admission (all yes/no classification). False: house prices, height v weight, spread rate (continuous values: linear/polynomial regression).
Common mistakes
- Python questions (2025 HSC exam Q19): process string inputs; use a selection structure; code must execute in the environment; use correct indentation; call functions in the main program after defining them.
- Python questions (2025 HSC exam Q26): replicate the grid; use the random library; use selection and looping to access the list; include a method of checking the number of values; use meaningful variable names and constants; avoid overly complex structures such as nested IF loops.
- Use the term exactly as the syllabus words it, then link it to the scenario in the question; the 2025 feedback repeatedly rewards using the stimulus.
- Answer the command word: outline gives main points only, explain gives a reason or cause, discuss gives both sides.
Related terms
Same group in the syllabus: Decision tree · Neural network · Linear regression · Polynomial regression · K-nearest neighbour (KNN) · OOP for ML programming · Neural network code (predict)