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Related Questions
- How can overfitting occur when using cross-validation for model selection?
- What are some common issues with the 'one standard error' rule for selecting the optimal model complexity?
- In what ways can cross-validation fail to detect overfitting when the training and test sets are not representative of the real data distribution?
- How does the choice of cross-validation fold count affect the model selection process and interpretability?
- What are some scenarios where cross-validation may not be the best method for model selection, and what alternative methods can be used?
- Can cross-validation lead to models that are overly complex or have poor generalization performance due to the way it handles model selection?
- How can the process of model selection using cross-validation lead to models that have poor interpretability due to the selection of overly complex models?
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