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MLA-C01 Practice Question: A data scientist wants to normalize a feature to…
A data scientist wants to normalize a feature to have a range between 0 and 1 for a neural network. Which scaling technique should be applied?
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
✓
MinMaxScaler
MinMaxScaler scales each feature to a given range, typically [0,1], by subtracting the minimum and dividing by the range. This makes it the correct choice for normalizing data to [0,1]. RobustScaler uses median and IQR, StandardScaler uses mean and standard deviation (resulting in zero mean, unit variance), and MaxAbsScaler scales to [-1,1] by dividing by the maximum absolute value. Therefore, only MinMaxScaler guarantees a [0,1] range.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
RobustScaler
Why it's wrong here
RobustScaler centres using the median and scales by the interquartile range, so outputs are not bounded to 0–1 and outliers are deliberately retained. It is tempting because it is the right choice when a feature contains significant outliers that would otherwise distort scaling.
- ✗
StandardScaler
Why it's wrong here
StandardScaler subtracts the mean and divides by the standard deviation, producing a distribution centred on zero with unit variance, not a bounded 0–1 range. It is tempting because it is the correct choice when a feature is approximately Gaussian and the algorithm assumes standardised inputs.
- ✗
MaxAbsScaler
Why it's wrong here
MaxAbsScaler divides each value by the maximum absolute value, mapping the range to −1 to 1 while preserving sparsity, so it does not produce a 0–1 range. It is tempting because it is the correct choice for sparse data where centring would destroy the zero entries.
- ✓
MinMaxScaler
Why this is correct
MinMaxScaler applies the transformation x' = (x - min) / (max - min), linearly mapping each feature onto the [0, 1] interval. This satisfies the stem's explicit 0-to-1 range requirement, unlike StandardScaler which centres on zero with unit variance and produces negative values.
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