What are the potential drawbacks of using k-fold Cross-Validation?
- Higher bias and low variance
- Increase in computation time and potential leakage of validation into training
- Lack of statistical estimation properties
- No drawbacks
k-fold Cross-Validation can increase computational time as the model is trained k times on different subsets of the data. Also, improper implementation can lead to data leakage between validation and training sets. It generally provides a more unbiased estimate of model performance but comes at the cost of increased computation.
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