Ultimate access to all questions.
Upgrade Now 🚀
Sign in to unlock AI tutor
You are working on a time series forecasting problem and have decided to use cross-validation to evaluate your model's performance. Which type of cross-validation is most appropriate for time series data, and why?
A
K-fold cross-validation, because it is a widely used technique for evaluating the performance of machine learning models.
B
Stratified cross-validation, because it ensures that each fold has a balanced representation of the target variable.
C
Time series cross-validation, because it takes into account the temporal ordering of the data.
D
Leave-one-out cross-validation, because it provides a more robust estimate of the model's performance by using a large number of folds.
