MLA-C01 Question 45
Select 3A data science team is training a machine learning model using Amazon SageMaker. They want to ensure that the model's performance is evaluated using a proper validation strategy to avoid overfitting. Which of the following strategies would be appropriate for this purpose?
- A
Using k-fold cross-validation
- B
Splitting data into training, validation, and test sets
- C
Using the entire dataset for both training and validation
- D
Randomly shuffling and splitting the dataset multiple times to average the performance metrics
- E
Using only the test data for validation purposes
Show answer and explanation
Correct answers: A, B, D
Explanation
Proper validation strategies, such as k-fold cross-validation, splitting data into training, validation, and test sets, or shuffling/splitting data multiple times, are crucial to evaluate machine learning models and prevent overfitting. Each method ensures that the model's performance is tested on unseen data, while maintaining a robust evaluation pipeline. Avoiding improper practices, such as using the entire dataset for training and validation or using test data for validation, is essential for producing reliable models.
- A. Correct.
Correct. K-fold cross-validation is a widely used validation technique that helps to assess model performance by splitting the data into k subsets and rotating the validation set across folds.
- B. Correct.
Correct. Splitting the data into training, validation, and test sets ensures that the model is evaluated on unseen data during validation and testing, reducing overfitting risk.
- C. Incorrect.
Incorrect. Using the entire dataset for both training and validation can lead to overfitting, as the model sees the same data during both phases.
- D. Correct.
Correct. Randomly shuffling and splitting the dataset multiple times (e.g., repeated cross-validation) provides a robust estimate of model performance by averaging results over multiple splits.
- E. Incorrect.
Incorrect. Using only the test data for validation purposes is not a recommended practice, as it prevents the use of a dedicated holdout test set to evaluate the final model performance.