Question 14
UnclassifiedSMOTE addresses class imbalance by:
Correct answer: B
Explanation
SMOTE stands for Synthetic Minority Over-sampling Technique, which creates new minority-class examples instead of duplicating existing ones. It does this by generating samples through interpolation between a minority point and its nearest minority neighbors, which increases minority representation and helps reduce class imbalance.
Why each option is right or wrong
A. Removing majority-class samples to make the classes equal in size.
B. Synthesizing new minority-class samples by interpolating between existing minority points and their nearest neighbors.
SMOTE (Synthetic Minority Over-sampling Technique) is the standard oversampling method that creates artificial minority observations rather than simply copying existing ones. In the algorithm, each new point is generated by selecting a minority-class instance and one of its k nearest minority neighbors, then placing the synthetic sample on the line segment between them; the usual default is k = 5 neighbors, which is why the minority class becomes denser without exact duplicates.
C. Reweighting the loss function to penalize minority-class errors more heavily.
D. Adding random noise to all features to increase dataset variability.