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MLA-C01 Practice Question: A data scientist needs to normalize numeric…
A data scientist needs to normalize numeric features for a deep learning model. The features have different scales and distributions, and the model uses gradient descent. Which scaling method is MOST appropriate?
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
✓
StandardScaler
StandardScaler standardizes features by removing the mean and scaling to unit variance, which works well with gradient descent even if features are not normally distributed. MinMaxScaler is sensitive to outliers.
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 uses median and IQR, robust to outliers but not always optimal for all distributions.
- ✗
MinMaxScaler
Why it's wrong here
MinMaxScaler scales to a fixed range, but outliers can compress the scale and affect convergence.
- ✗
MaxAbsScaler
Why it's wrong here
MaxAbsScaler scales by maximum absolute value, which assumes sparse data and may not be suitable.
- ✓
StandardScaler
Why this is correct
StandardScaler centers and scales features, making gradient descent converge faster and more reliably.
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