Cross-validation, such as _______-fold cross-validation, can help in detecting and preventing overfitting.
- 10
- 3
- 5
- any number
Any number of folds can be used in cross-validation, although commonly used numbers include 5 and 10. Cross-validation helps in model validation and prevents overfitting.
Loading...
Related Quiz
- One of the challenges in DQN is that small updates to Q values can lead to significant changes in the policy, making the learning process highly ________.
- If a model has low bias and high variance, it is likely that the model is ________.
- Explain the importance of feature selection and engineering in building a Machine Learning model.
- You are working on a project where you have an abundance of features. How do you decide which features to include in your model and why?
- You are working on a dataset with an imbalanced class distribution. How would you apply Cross-Validation to ensure that each fold maintains the same class distribution?