Answer-first summary for fast verification
Answer: No
The Scale and Reduce sampling mode in Azure Machine Learning is designed for data transformation tasks such as clipping, binning, and normalizing numerical values, not for addressing class imbalance. The community discussion, with multiple upvoted comments (e.g., 7 upvotes for azurelearner666's comment), confirms that SMOTE (Synthetic Minority Oversampling Technique) is the appropriate method for compensating for class imbalance by generating synthetic samples for the minority class. Therefore, the proposed solution does not meet the goal, making 'No' the correct answer.
Author: LeetQuiz Editorial Team
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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 Scale and Reduce sampling mode.
Does this solution meet the goal?
A
Yes
B
No
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