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Related Questions
- What are the primary advantages of using k-fold cross-validation in machine learning model evaluation?
- How does the number of folds in k-fold cross-validation impact the variance of the model's performance estimates?
- What is the relationship between the number of folds and the computational resources required for k-fold cross-validation?
- Can you explain the concept of overfitting in the context of k-fold cross-validation and how increasing the number of folds can mitigate it?
- How does the choice of k in k-fold cross-validation affect the trade-off between bias and variance in model evaluation?
- What are some common pitfalls to avoid when selecting the number of folds for k-fold cross-validation?
- Can you describe a scenario where a very large number of folds may be beneficial for k-fold cross-validation?
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