Answer-first summary for fast verification
Answer: No
The solution does NOT meet the goal. Principal Component Analysis (PCA) is a dimensionality reduction technique, not a sampling technique for addressing class imbalance. PCA transforms features into principal components to reduce dimensionality while preserving variance, but it does not alter the class distribution or balance the dataset. For class imbalance, appropriate sampling strategies include oversampling the minority class (e.g., using SMOTE - Synthetic Minority Over-sampling Technique), undersampling the majority class, or using ensemble methods. The community discussion confirms this with 100% consensus on 'No' (answer B), noting that PCA is deprecated for anomaly detection in Azure ML and emphasizing SMOTE as a correct alternative for imbalance handling.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
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.
Solution: You use the Principal Component Analysis (PCA) sampling mode.
Does this solution meet the goal?
A
Yes
B
No
No comments yet.