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Answer: Synthetic Minority Over-sampling Technique (SMOTE)
The correct choice is **Synthetic Minority Over-sampling Technique (SMOTE)**. SMOTE is specifically designed to address class imbalance by oversampling the minority class. In Spark ML, SMOTE can be utilized to create synthetic samples for the minority class, thereby balancing the class distribution. This approach significantly improves the model's learning capability from the minority class, making it highly effective for imbalanced datasets in machine learning projects.
Author: LeetQuiz Editorial Team
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Your team is working on a machine learning project with highly imbalanced classes. Which Spark ML algorithm would you choose to tackle the class imbalance and enhance model performance?
A
Random Forest
B
Decision Trees
C
Synthetic Minority Over-sampling Technique (SMOTE)
D
Support Vector Machines
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