Answer-first summary for fast verification
Answer: Yes
The proposed solution meets the goal because SMOTE (Synthetic Minority Oversampling Technique) is specifically designed to address class imbalance by generating synthetic samples for the minority class. This technique creates new, synthetic examples that are similar to existing minority class instances, rather than simply duplicating them, which helps prevent overfitting. The community discussion shows unanimous agreement (100% for option A) with multiple comments confirming that SMOTE is appropriate when a class is underrepresented, and references to Microsoft's official documentation support this as a valid approach in Azure Machine Learning Studio.
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You are creating a new experiment in Azure Machine Learning Studio. The training set has a significant class imbalance, with one class having far fewer observations than the others.
You need to choose a suitable data sampling strategy to address this imbalance.
Proposed Solution: Use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.
Does the proposed solution meet the goal?
A
Yes
B
No
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