Explain the Bias-Variance tradeoff in the context of Cross-Validation.
- Increasing k decreases bias but may increase variance
- Increasing k decreases both bias and variance
- Increasing k increases bias but decreases variance
- Increasing k increases both bias and variance
The Bias-Variance tradeoff in the context of k-fold Cross-Validation refers to the balance between bias (error due to overly simplistic assumptions) and variance (error due to excessive complexity). Increasing k generally decreases bias since more data is used for training, but it may lead to an increase in variance as the validation set becomes more similar to the training set.
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