MLS-C01 Question 242
Select 3You are building a machine learning model using Amazon SageMaker, and you want to ensure the model generalizes well on unseen data. To validate the model's performance, you decide to use k-fold cross-validation. Which of the following steps are essential for performing k-fold cross-validation in this scenario?
- A
Split the dataset into k equally-sized folds and use each fold as a validation set while training on the remaining folds.
- B
Ensure that the dataset is shuffled before splitting it into folds to avoid bias in the training and validation sets.
- C
Train the model on the entire dataset first to establish a baseline before performing k-fold cross-validation.
- D
Compute the average performance metric (e.g., accuracy, F1-score) across all k folds to evaluate the model.
- E
Use only one fold for validation and discard the rest of the data to speed up the process.
Show answer and explanation
Correct answers: A, B, D
Explanation
K-fold cross-validation is a robust method for evaluating a model's performance by dividing the dataset into k folds and iteratively using each fold as a validation set while training on the remaining folds. This process ensures that all data is used for both training and validation, providing a comprehensive assessment of the model's generalization ability. Shuffling the data before splitting into folds and averaging the performance metrics across folds are critical steps to ensure unbiased and reliable results.
- A. Correct.
Correct: Splitting the dataset into k folds and iteratively using each fold as a validation set while training on the remaining folds is the core concept of k-fold cross-validation.
- B. Correct.
Correct: Shuffling the dataset before splitting it into folds helps ensure that the data distribution is uniform across folds, avoiding biased results.
- C. Incorrect.
Incorrect: Training the model on the entire dataset before performing k-fold cross-validation is not an essential step. Cross-validation is performed to estimate model performance without relying on a single train-validation split.
- D. Correct.
Correct: Computing the average performance metric across all k folds provides a robust estimate of the model's generalization performance.
- E. Incorrect.
Incorrect: Using only one fold for validation and discarding the rest of the data is not a valid approach for k-fold cross-validation. The purpose of k-fold cross-validation is to use all data for training and validation across iterations.