Answer: Synthetic Minority Oversampling Technique (SMOTE)

The question specifically addresses handling an imbalanced dataset in a classification task to improve accuracy. Option D (SMOTE) is the correct choice because it directly tackles class imbalance by generating synthetic samples for the minority class, which helps prevent model bias toward the majority class. The community discussion shows unanimous support for D (100% of answers), with references to Azure ML documentation confirming SMOTE's availability and relevance. Other options are unsuitable: A (Permutation Feature Importance) and B (Filter Based Feature Selection) focus on feature selection, not imbalance handling, and C (Fisher Linear Discriminant Analysis) is a dimensionality reduction technique, not an imbalance correction method.

Author: LeetQuiz Editorial Team