Question

Which of the following statements is correct about cross-validation?

a. Cross-validation is a standard practice for validating a predictive model's performance
b. Cross-validation involves swapping datasets with a friend
c. It is a way of testing your model on data it has seen before
d. There is only one way to do cross-validation

Answer 1

Cross-validation is a standard practice for validating a predictive model's performance by dividing the dataset into subsets and testing the model on unseen data. It does not involve swapping datasets with a friend, testing on seen data, or having only one method.

Step-by-step explanation:

Cross-validation is a standard practice for validating a predictive model's performance. It involves dividing the dataset into multiple subsets, training the model on one subset, and testing it on the remaining subsets. This process helps determine how well the model will perform on new, unseen data. There are different types of cross-validation, such as k-fold cross-validation and leave-one-out cross-validation, which offer flexibility in evaluating model performance.

Cross-validation does not involve swapping datasets with a friend, as stated in option b. Option c, testing the model on data it has seen before, is incorrect because cross-validation ensures that the testing data is separate from the training data. Finally, option d is incorrect because there are multiple ways to perform cross-validation, as mentioned earlier.