When working with smaller data sets (N<200), which method is preferred to perform honest assessment?
When working with smaller data sets (N<200), which method is preferred to perform honest assessment?
When dealing with smaller data sets (N<200), it's crucial to optimize the use of available data. K-fold cross validation is preferred because it allows every data point to be used for both training and validation, thereby providing a more robust estimation of model performance and a better generalization to unseen data.
Pretty sure the answer is B.
B is a better answer