Answer: Synthetic Minority Oversampling Technique (SMOTE)

The question asks for an Azure Machine Learning Studio module to handle an imbalanced dataset in a classification task. Option D, Synthetic Minority Oversampling Technique (SMOTE), is specifically designed to address class imbalance by generating synthetic samples for the minority class, which helps improve classification accuracy. The community discussion shows unanimous consensus (100% of answers and multiple comments) supporting D, with references to Azure ML documentation confirming SMOTE's availability and appropriateness. Other options are unsuitable: A (Permutation Feature Importance) and B (Filter Based Feature Selection) are for feature selection, not imbalance handling, and C (Fisher Linear Discriminant Analysis) is a dimensionality reduction technique, not directly for imbalance correction.

Author: LeetQuiz Editorial Team