MLA-C01 Question 395
Select 3A data scientist is training a machine learning model using Amazon SageMaker. The dataset is highly imbalanced, and the scientist wants to improve the model's ability to correctly predict the minority class. Which of the following AWS-provided techniques or features should the data scientist use to address this imbalance?
- A
Use the 'WeightedLoss' parameter in the SageMaker built-in algorithms to assign higher weights to the minority class
- B
Oversample the minority class using Amazon SageMaker Data Wrangler
- C
Leverage the 'class_weight' argument in the SageMaker XGBoost algorithm to adjust class importance
- D
Set up a custom evaluation metric using Amazon SageMaker Clarify to focus on the minority class's precision
- E
Apply SMOTE (Synthetic Minority Oversampling Technique) directly within SageMaker's built-in algorithms
Show answer and explanation
Correct answers: A, B, C
Explanation
Handling data imbalance is crucial for improving model performance on minority classes. AWS provides several features to address the issue, such as the 'WeightedLoss' parameter in built-in algorithms, oversampling via SageMaker Data Wrangler, and the 'class_weight' argument in SageMaker XGBoost. These techniques help adjust the model's focus towards the minority class. However, some approaches, like SMOTE, require custom implementation, and tools like SageMaker Clarify are designed for bias detection, not imbalanced dataset handling.
- A. Correct.
Correct: Many SageMaker built-in algorithms support the 'WeightedLoss' parameter that allows assigning higher weights to the minority class to improve model performance for imbalanced datasets.
- B. Correct.
Correct: Amazon SageMaker Data Wrangler can be used to preprocess data, including oversampling techniques to balance the dataset.
- C. Correct.
Correct: The SageMaker XGBoost algorithm supports the 'class_weight' argument to adjust class importance, which is useful for handling imbalanced datasets.
- D. Incorrect.
Incorrect: While SageMaker Clarify is useful for identifying bias and fairness issues, it does not directly help in improving a model's performance on imbalanced datasets.
- E. Incorrect.
Incorrect: SMOTE is not natively supported within SageMaker built-in algorithms. SMOTE would need to be implemented manually outside of the built-in algorithms, potentially during data preprocessing.