DA0-002 Data Analysis Practice Question
Which TWO of the following are appropriate uses of min-max normalisation?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Scaling features to a range of 0 to 1
Min-max normalisation scales data to a fixed range (often 0-1), useful for distance-based algorithms like k-NN and neural networks. Standardisation (Z-score) is better for algorithms assuming Gaussian distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Transforming data to have mean 0 and standard deviation 1
Why it's wrong here
That describes Z-score standardisation.
- ✓
Scaling features to a range of 0 to 1
Why this is correct
Correct: Min-max normalisation scales to [0,1].
- ✗
Preparing data for linear regression with normally distributed residuals
Why it's wrong here
Linear regression typically does not require normalisation; if needed, standardisation is preferred.
- ✓
Preparing data for k-nearest neighbours algorithm
Why this is correct
Correct: k-NN relies on distances, so normalisation helps.
- ✗
Handling missing values
Why it's wrong here
Normalisation does not handle missing values.
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