A researcher applies SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance before training. What synthetic minority samples does SMOTE generate?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because SMOTE generates synthetic minority observations by selecting a random minority sample, choosing one of its k nearest minority-class neighbors, and interpolating a new point along the line segment between them: new_sample = minority_obs + rand * (neighbor - minority_obs). A (copies with jitter) is not SMOTE but is similar to random oversampling with noise.
Full explanation below image
Full Explanation
B is correct because SMOTE generates synthetic minority observations by selecting a random minority sample, choosing one of its k nearest minority-class neighbors, and interpolating a new point along the line segment between them: new_sample = minority_obs + rand * (neighbor - minority_obs). A (copies with jitter) is not SMOTE but is similar to random oversampling with noise. C (Gaussian sampling) describes a different generative approach. D (label flipping) would corrupt the data.