Answer-first summary for fast verification
Answer: No
The Scale and Reduce sampling mode in Azure Machine Learning is designed for data preprocessing tasks like clipping, binning, and normalizing numerical values, not for addressing class imbalance. The community discussion (with high upvotes) and official documentation confirm that SMOTE (Synthetic Minority Oversampling Technique) is the appropriate method for handling class imbalance by generating synthetic samples for the minority class, rather than simply scaling or reducing data. Therefore, using Scale and Reduce does not meet the goal of compensating for class imbalance.
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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