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Related Questions
- What is the purpose of cross-validation in machine learning?
- How does k-fold cross-validation work, and what are its benefits and drawbacks?
- What are the common evaluation metrics used to detect overfitting during cross-validation?
- How can you choose the optimal number of folds for k-fold cross-validation?
- Can you explain the difference between stratified and non-stratified cross-validation?
- How does cross-validation help in identifying feature selection or engineering issues that may cause overfitting?
- Are there any scenarios where cross-validation might not be suitable for detecting overfitting, and what are the alternatives?
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