MLS-C01 Exploratory Data Analysis Practice Question
Which TWO of the following are benefits of feature scaling for machine learning algorithms?
⚠ Common exam trap
Watch out — candidates often assume feature scaling universally improves all algorithms, but The MLS-C01 exam specifically tests that tree-based models (like decision trees) are scale-invariant, making option C a common distractor.
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
✓
Faster convergence of gradient descent
Feature scaling, typically via standardization (z-score) or min-max normalization, ensures that gradient descent converges faster. Without scaling, features with larger numerical ranges dominate the gradient updates, causing the algorithm to oscillate and require more iterations to reach the optimum. Scaling produces a more spherical contour of the loss function, allowing gradient descent to take more direct steps toward the minimum.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Eliminates the effect of outliers
Why it's wrong here
Scaling does not remove outliers; robust scaling may reduce but not eliminate.
- ✗
Reduces the need for feature selection
Why it's wrong here
Scaling does not eliminate irrelevant features.
- ✗
Improves performance of decision tree algorithms
Why it's wrong here
Decision trees are not affected by feature scaling.
- ✓
Faster convergence of gradient descent
Why this is correct
Scaling ensures all features contribute equally to the gradient.
- ✓
Prevents features with larger magnitudes from dominating distance-based algorithms
Why this is correct
Algorithms like k-NN and SVM are sensitive to feature scales.
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